diff --git a/.github/workflows/redo_regressions.yml b/.github/workflows/redo_regressions.yml
index c7111631aa..77df4959a2 100644
--- a/.github/workflows/redo_regressions.yml
+++ b/.github/workflows/redo_regressions.yml
@@ -58,6 +58,7 @@ jobs:
       shell: bash -l {0}
       run: |
         git config user.name "Redo regressions bot"
+        git config user.email "<>"
         git add extra_tests/regression_fits/*
         git status
         git commit -m "Automatically regenerated regressions from PR ${{ github.event.number }}, branch ${{ github.event.pull_request.head.ref }}."
diff --git a/conda-recipe/conda_build_config.yaml b/conda-recipe/conda_build_config.yaml
index 95a95fa5b0..405317556b 100644
--- a/conda-recipe/conda_build_config.yaml
+++ b/conda-recipe/conda_build_config.yaml
@@ -1,7 +1,7 @@
 #For some reason the resolver decides to use an old version of numpy
 #without this
 numpy:
-    - 1.24
+    - 1.26
 
 pin_run_as_build:
     lhapdf: x.x.x
diff --git a/extra_tests/regression_checks.py b/extra_tests/regression_checks.py
index ceb04cd475..54f2b2e6cd 100644
--- a/extra_tests/regression_checks.py
+++ b/extra_tests/regression_checks.py
@@ -28,6 +28,7 @@
     "no_csr": 613,
     "polarized_evol": 34,
     "multi_dense": 316,
+    "t0theoryid": 100,
 }
 
 
diff --git a/extra_tests/regression_fits/central_16.json b/extra_tests/regression_fits/central_16.json
index f4da97612e..c1ed6fed3b 100644
--- a/extra_tests/regression_fits/central_16.json
+++ b/extra_tests/regression_fits/central_16.json
@@ -71,25 +71,25 @@
   ],
   "timing": {
     "walltime": {
-      "Total": 12.018421173095703,
+      "Total": 11.461867332458496,
       "start": 0.0,
-      "replica_set": 0.23227167129516602,
-      "replica_fitted": 12.018356561660767,
-      "replica_set_to_replica_fitted": 11.7860848903656
+      "replica_set": 0.22239995002746582,
+      "replica_fitted": 11.46180772781372,
+      "replica_set_to_replica_fitted": 11.239407777786255
     },
     "cputime": {
-      "Total": 13.647957485,
+      "Total": 13.093227772000002,
       "start": 0.0,
-      "replica_set": 0.23026263799999924,
-      "replica_fitted": 13.647891120999997,
-      "replica_set_to_replica_fitted": 13.417628482999998
+      "replica_set": 0.22140489500000005,
+      "replica_fitted": 13.093166188000001,
+      "replica_set_to_replica_fitted": 12.871761293
     }
   },
   "version": {
     "keras": "3.6.0",
-    "tensorflow": "2.17.0, mkl=False",
+    "tensorflow": "2.18.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1366.dev0+f934deb20",
-    "validphys": "4.0.9.post1366.dev0+f934deb20"
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
   }
 }
diff --git a/extra_tests/regression_fits/diagonal_45.json b/extra_tests/regression_fits/diagonal_45.json
index 81ec009cbd..abe08e2d4b 100644
--- a/extra_tests/regression_fits/diagonal_45.json
+++ b/extra_tests/regression_fits/diagonal_45.json
@@ -71,25 +71,25 @@
   ],
   "timing": {
     "walltime": {
-      "Total": 10.89296817779541,
+      "Total": 10.632083654403687,
       "start": 0.0,
-      "replica_set": 0.2278742790222168,
-      "replica_fitted": 10.892873764038086,
-      "replica_set_to_replica_fitted": 10.66499948501587
+      "replica_set": 0.24991393089294434,
+      "replica_fitted": 10.63199520111084,
+      "replica_set_to_replica_fitted": 10.382081270217896
     },
     "cputime": {
-      "Total": 12.396652105999998,
+      "Total": 12.098074116,
       "start": 0.0,
-      "replica_set": 0.227109338,
-      "replica_fitted": 12.396556207,
-      "replica_set_to_replica_fitted": 12.169446869000001
+      "replica_set": 0.24743486699999995,
+      "replica_fitted": 12.097983746999997,
+      "replica_set_to_replica_fitted": 11.850548879999998
     }
   },
   "version": {
     "keras": "3.6.0",
-    "tensorflow": "2.17.0, mkl=False",
+    "tensorflow": "2.18.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1366.dev0+f934deb20",
-    "validphys": "4.0.9.post1366.dev0+f934deb20"
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
   }
 }
diff --git a/extra_tests/regression_fits/feature_scaling_81.json b/extra_tests/regression_fits/feature_scaling_81.json
index f8a062636c..f99fccd05a 100644
--- a/extra_tests/regression_fits/feature_scaling_81.json
+++ b/extra_tests/regression_fits/feature_scaling_81.json
@@ -71,25 +71,25 @@
   ],
   "timing": {
     "walltime": {
-      "Total": 11.31751823425293,
+      "Total": 11.171345472335815,
       "start": 0.0,
-      "replica_set": 0.23138642311096191,
-      "replica_fitted": 11.317461729049683,
-      "replica_set_to_replica_fitted": 11.08607530593872
+      "replica_set": 0.2302110195159912,
+      "replica_fitted": 11.171200513839722,
+      "replica_set_to_replica_fitted": 10.94098949432373
     },
     "cputime": {
-      "Total": 12.811329942,
+      "Total": 12.607453065000001,
       "start": 0.0,
-      "replica_set": 0.2290858440000001,
-      "replica_fitted": 12.811271753,
-      "replica_set_to_replica_fitted": 12.582185909
+      "replica_set": 0.22927526099999973,
+      "replica_fitted": 12.607306401999999,
+      "replica_set_to_replica_fitted": 12.378031141
     }
   },
   "version": {
     "keras": "3.6.0",
-    "tensorflow": "2.17.0, mkl=False",
+    "tensorflow": "2.18.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1366.dev0+f934deb20",
-    "validphys": "4.0.9.post1366.dev0+f934deb20"
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
   }
 }
diff --git a/extra_tests/regression_fits/flavour_29.json b/extra_tests/regression_fits/flavour_29.json
index c4d7bed4a3..44816b702f 100644
--- a/extra_tests/regression_fits/flavour_29.json
+++ b/extra_tests/regression_fits/flavour_29.json
@@ -71,25 +71,25 @@
   ],
   "timing": {
     "walltime": {
-      "Total": 4.7116029262542725,
+      "Total": 4.690255403518677,
       "start": 0.0,
-      "replica_set": 0.22231054306030273,
-      "replica_fitted": 4.711346387863159,
-      "replica_set_to_replica_fitted": 4.4890358448028564
+      "replica_set": 0.23582863807678223,
+      "replica_fitted": 4.690072774887085,
+      "replica_set_to_replica_fitted": 4.454244136810303
     },
     "cputime": {
-      "Total": 4.862365402999999,
+      "Total": 4.831552295,
       "start": 0.0,
-      "replica_set": 0.2213146479999999,
-      "replica_fitted": 4.862106009999999,
-      "replica_set_to_replica_fitted": 4.640791361999999
+      "replica_set": 0.2345493980000004,
+      "replica_fitted": 4.831367581,
+      "replica_set_to_replica_fitted": 4.596818183
     }
   },
   "version": {
     "keras": "3.6.0",
-    "tensorflow": "2.17.0, mkl=False",
+    "tensorflow": "2.18.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1366.dev0+f934deb20",
-    "validphys": "4.0.9.post1366.dev0+f934deb20"
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
   }
 }
diff --git a/extra_tests/regression_fits/multi_dense_316.json b/extra_tests/regression_fits/multi_dense_316.json
index 0c14de95a0..d1ff7d0767 100644
--- a/extra_tests/regression_fits/multi_dense_316.json
+++ b/extra_tests/regression_fits/multi_dense_316.json
@@ -71,25 +71,25 @@
   ],
   "timing": {
     "walltime": {
-      "Total": 11.636826753616333,
+      "Total": 11.391953229904175,
       "start": 0.0,
-      "replica_set": 0.22696971893310547,
-      "replica_fitted": 11.63676381111145,
-      "replica_set_to_replica_fitted": 11.409794092178345
+      "replica_set": 0.22628402709960938,
+      "replica_fitted": 11.391895294189453,
+      "replica_set_to_replica_fitted": 11.165611267089844
     },
     "cputime": {
-      "Total": 13.214493326,
+      "Total": 13.023389705,
       "start": 0.0,
-      "replica_set": 0.22588198299999984,
-      "replica_fitted": 13.214427934,
-      "replica_set_to_replica_fitted": 12.988545951
+      "replica_set": 0.2253089749999999,
+      "replica_fitted": 13.023330024,
+      "replica_set_to_replica_fitted": 12.798021048999999
     }
   },
   "version": {
     "keras": "3.6.0",
-    "tensorflow": "2.17.0, mkl=False",
+    "tensorflow": "2.18.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1366.dev0+f934deb20",
-    "validphys": "4.0.9.post1366.dev0+f934deb20"
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
   }
 }
diff --git a/extra_tests/regression_fits/no_csr_613.json b/extra_tests/regression_fits/no_csr_613.json
index d78cd10c37..84a65a38e2 100644
--- a/extra_tests/regression_fits/no_csr_613.json
+++ b/extra_tests/regression_fits/no_csr_613.json
@@ -77,25 +77,25 @@
   ],
   "timing": {
     "walltime": {
-      "Total": 9.675678253173828,
+      "Total": 9.481953382492065,
       "start": 0.0,
-      "replica_set": 0.23419880867004395,
-      "replica_fitted": 9.675537109375,
-      "replica_set_to_replica_fitted": 9.441338300704956
+      "replica_set": 0.22993159294128418,
+      "replica_fitted": 9.481811761856079,
+      "replica_set_to_replica_fitted": 9.251880168914795
     },
     "cputime": {
-      "Total": 10.798555852999998,
+      "Total": 10.565282932000002,
       "start": 0.0,
-      "replica_set": 0.23191265599999955,
-      "replica_fitted": 10.798412807,
-      "replica_set_to_replica_fitted": 10.566500151
+      "replica_set": 0.22892762300000058,
+      "replica_fitted": 10.565139715,
+      "replica_set_to_replica_fitted": 10.336212092
     }
   },
   "version": {
     "keras": "3.6.0",
-    "tensorflow": "2.17.0, mkl=False",
+    "tensorflow": "2.18.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1366.dev0+f934deb20",
-    "validphys": "4.0.9.post1366.dev0+f934deb20"
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
   }
 }
diff --git a/extra_tests/regression_fits/no_lagrange_27.json b/extra_tests/regression_fits/no_lagrange_27.json
index 40d5e18c14..a8557c1743 100644
--- a/extra_tests/regression_fits/no_lagrange_27.json
+++ b/extra_tests/regression_fits/no_lagrange_27.json
@@ -71,25 +71,25 @@
   ],
   "timing": {
     "walltime": {
-      "Total": 15.686562776565552,
+      "Total": 15.563487768173218,
       "start": 0.0,
-      "replica_set": 0.22611141204833984,
-      "replica_fitted": 15.686427593231201,
-      "replica_set_to_replica_fitted": 15.460316181182861
+      "replica_set": 0.23473668098449707,
+      "replica_fitted": 15.563342332839966,
+      "replica_set_to_replica_fitted": 15.328605651855469
     },
     "cputime": {
-      "Total": 17.064158211,
+      "Total": 16.861277111,
       "start": 0.0,
-      "replica_set": 0.22520821400000024,
-      "replica_fitted": 17.064021315999998,
-      "replica_set_to_replica_fitted": 16.838813102
+      "replica_set": 0.232657691,
+      "replica_fitted": 16.861129706,
+      "replica_set_to_replica_fitted": 16.628472015
     }
   },
   "version": {
     "keras": "3.6.0",
-    "tensorflow": "2.17.0, mkl=False",
+    "tensorflow": "2.18.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1366.dev0+f934deb20",
-    "validphys": "4.0.9.post1366.dev0+f934deb20"
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
   }
 }
diff --git a/extra_tests/regression_fits/no_msr_92.json b/extra_tests/regression_fits/no_msr_92.json
index 2c27d959d3..620a14064a 100644
--- a/extra_tests/regression_fits/no_msr_92.json
+++ b/extra_tests/regression_fits/no_msr_92.json
@@ -71,25 +71,25 @@
   ],
   "timing": {
     "walltime": {
-      "Total": 21.57416319847107,
+      "Total": 21.4597225189209,
       "start": 0.0,
-      "replica_set": 0.2312939167022705,
-      "replica_fitted": 21.574037075042725,
-      "replica_set_to_replica_fitted": 21.342743158340454
+      "replica_set": 0.23497629165649414,
+      "replica_fitted": 21.459640741348267,
+      "replica_set_to_replica_fitted": 21.224664449691772
     },
     "cputime": {
-      "Total": 23.208116806,
+      "Total": 23.170773932000003,
       "start": 0.0,
-      "replica_set": 0.22919140999999943,
-      "replica_fitted": 23.207989188,
-      "replica_set_to_replica_fitted": 22.978797778
+      "replica_set": 0.23272350500000005,
+      "replica_fitted": 23.170689936000002,
+      "replica_set_to_replica_fitted": 22.937966431
     }
   },
   "version": {
     "keras": "3.6.0",
-    "tensorflow": "2.17.0, mkl=False",
+    "tensorflow": "2.18.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1366.dev0+f934deb20",
-    "validphys": "4.0.9.post1366.dev0+f934deb20"
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
   }
 }
diff --git a/extra_tests/regression_fits/no_sumrules_18.json b/extra_tests/regression_fits/no_sumrules_18.json
index 73c2de3cff..45964fe117 100644
--- a/extra_tests/regression_fits/no_sumrules_18.json
+++ b/extra_tests/regression_fits/no_sumrules_18.json
@@ -71,25 +71,25 @@
   ],
   "timing": {
     "walltime": {
-      "Total": 17.569430351257324,
+      "Total": 17.269383192062378,
       "start": 0.0,
-      "replica_set": 0.23427820205688477,
-      "replica_fitted": 17.569316625595093,
-      "replica_set_to_replica_fitted": 17.335038423538208
+      "replica_set": 0.23167967796325684,
+      "replica_fitted": 17.269216537475586,
+      "replica_set_to_replica_fitted": 17.03753685951233
     },
     "cputime": {
-      "Total": 19.022983414,
+      "Total": 18.788655262,
       "start": 0.0,
-      "replica_set": 0.23228657200000047,
-      "replica_fitted": 19.022868079,
-      "replica_set_to_replica_fitted": 18.790581507
+      "replica_set": 0.23061819099999958,
+      "replica_fitted": 18.788486498,
+      "replica_set_to_replica_fitted": 18.557868307
     }
   },
   "version": {
     "keras": "3.6.0",
-    "tensorflow": "2.17.0, mkl=False",
+    "tensorflow": "2.18.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1366.dev0+f934deb20",
-    "validphys": "4.0.9.post1366.dev0+f934deb20"
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
   }
 }
diff --git a/extra_tests/regression_fits/no_vsr_54.json b/extra_tests/regression_fits/no_vsr_54.json
index 0ba62ec328..20d01b61bf 100644
--- a/extra_tests/regression_fits/no_vsr_54.json
+++ b/extra_tests/regression_fits/no_vsr_54.json
@@ -71,25 +71,25 @@
   ],
   "timing": {
     "walltime": {
-      "Total": 11.739926099777222,
+      "Total": 11.414241790771484,
       "start": 0.0,
-      "replica_set": 0.22898173332214355,
-      "replica_fitted": 11.739850759506226,
-      "replica_set_to_replica_fitted": 11.510869026184082
+      "replica_set": 0.22046661376953125,
+      "replica_fitted": 11.414183139801025,
+      "replica_set_to_replica_fitted": 11.193716526031494
     },
     "cputime": {
-      "Total": 13.339112153000002,
+      "Total": 13.054506966,
       "start": 0.0,
-      "replica_set": 0.22787525900000016,
-      "replica_fitted": 13.339034288,
-      "replica_set_to_replica_fitted": 13.111159029
+      "replica_set": 0.21948998699999933,
+      "replica_fitted": 13.054446142999998,
+      "replica_set_to_replica_fitted": 12.834956155999999
     }
   },
   "version": {
     "keras": "3.6.0",
-    "tensorflow": "2.17.0, mkl=False",
+    "tensorflow": "2.18.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1366.dev0+f934deb20",
-    "validphys": "4.0.9.post1366.dev0+f934deb20"
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
   }
 }
diff --git a/extra_tests/regression_fits/normal_fit_72.json b/extra_tests/regression_fits/normal_fit_72.json
index f83d20b956..a394a1412e 100644
--- a/extra_tests/regression_fits/normal_fit_72.json
+++ b/extra_tests/regression_fits/normal_fit_72.json
@@ -71,25 +71,25 @@
   ],
   "timing": {
     "walltime": {
-      "Total": 17.19196391105652,
+      "Total": 16.82194948196411,
       "start": 0.0,
-      "replica_set": 0.2349693775177002,
-      "replica_fitted": 17.191868543624878,
-      "replica_set_to_replica_fitted": 16.956899166107178
+      "replica_set": 0.22348880767822266,
+      "replica_fitted": 16.821776866912842,
+      "replica_set_to_replica_fitted": 16.59828805923462
     },
     "cputime": {
-      "Total": 19.64416452,
+      "Total": 19.175740587,
       "start": 0.0,
-      "replica_set": 0.23289664700000046,
-      "replica_fitted": 19.644066357,
-      "replica_set_to_replica_fitted": 19.41116971
+      "replica_set": 0.22243870600000015,
+      "replica_fitted": 19.17556551,
+      "replica_set_to_replica_fitted": 18.953126804
     }
   },
   "version": {
     "keras": "3.6.0",
-    "tensorflow": "2.17.0, mkl=False",
+    "tensorflow": "2.18.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1366.dev0+f934deb20",
-    "validphys": "4.0.9.post1366.dev0+f934deb20"
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
   }
 }
diff --git a/extra_tests/regression_fits/polarized_evol_34.json b/extra_tests/regression_fits/polarized_evol_34.json
index a4703d8d8b..6bf46dd77e 100644
--- a/extra_tests/regression_fits/polarized_evol_34.json
+++ b/extra_tests/regression_fits/polarized_evol_34.json
@@ -47,25 +47,25 @@
   ],
   "timing": {
     "walltime": {
-      "Total": 9.182395219802856,
+      "Total": 9.003463745117188,
       "start": 0.0,
-      "replica_set": 0.23716163635253906,
-      "replica_fitted": 9.182160377502441,
-      "replica_set_to_replica_fitted": 8.944998741149902
+      "replica_set": 0.2323012351989746,
+      "replica_fitted": 9.003390312194824,
+      "replica_set_to_replica_fitted": 8.77108907699585
     },
     "cputime": {
-      "Total": 9.966191528,
+      "Total": 9.724381046999998,
       "start": 0.0,
-      "replica_set": 0.23521285599999953,
-      "replica_fitted": 9.965954095,
-      "replica_set_to_replica_fitted": 9.730741239
+      "replica_set": 0.2303084740000001,
+      "replica_fitted": 9.724305615999999,
+      "replica_set_to_replica_fitted": 9.493997142
     }
   },
   "version": {
     "keras": "3.6.0",
-    "tensorflow": "2.17.0, mkl=False",
+    "tensorflow": "2.18.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1366.dev0+f934deb20",
-    "validphys": "4.0.9.post1366.dev0+f934deb20"
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
   }
 }
diff --git a/extra_tests/regression_fits/t0theoryid.yml b/extra_tests/regression_fits/t0theoryid.yml
new file mode 100644
index 0000000000..89da10f650
--- /dev/null
+++ b/extra_tests/regression_fits/t0theoryid.yml
@@ -0,0 +1,81 @@
+#
+# Configuration file for n3fit regression tests
+# This runcard includes two DIS datasets, one Hadronic dataset
+# and two positivity datasets
+#
+
+############################################################
+description: n3fit regression test
+
+############################################################
+# frac: training fraction
+# ewk: apply ewk k-factors
+# sys: systematics treatment (see systypes)
+dataset_inputs:
+  - {dataset: NMC_NC_NOTFIXED_P_EM-SIGMARED, frac: 0.5, variant: legacy}
+  - {dataset: SLAC_NC_NOTFIXED_P_EM-F2, frac: 0.5, variant: legacy}
+  - {dataset: CMS_Z0J_8TEV_PT-Y, frac: 0.5, cfac: [QCD], variant: legacy_10}
+  - {dataset: ATLAS_TTBAR_8TEV_TOT_X-SEC, frac: 1.0, cfac: [QCD], variant: legacy}
+
+############################################################
+datacuts:
+  t0pdfset: NNPDF40_nnlo_as_01180     # PDF set to generate t0 covmat
+  q2min        : 3.49                # Q2 minimum
+  w2min        : 12.5                # W2 minimum
+
+############################################################
+theory:
+  theoryid: 399        # database id
+  t0theoryid: 398
+
+############################################################
+genrep: False    # on = generate MC replicas, False = use real data
+trvlseed: 3
+nnseed: 2
+mcseed: 1
+
+load: "weights.weights.h5"
+
+parameters: # This defines the parameter dictionary that is passed to the Model Trainer
+  nodes_per_layer: [15, 10, 8]
+  activation_per_layer: ['sigmoid', 'tanh', 'linear']
+  initializer: 'glorot_normal'
+  optimizer:
+    optimizer_name: 'RMSprop'
+    learning_rate: 0.00001
+    clipnorm: 1.0
+  epochs: 1100
+  positivity:
+    multiplier: 1.05
+    initial: 1.5
+  stopping_patience: 0.10 # percentage of the number of epochs
+  layer_type: 'dense'
+  dropout: 0.0
+  threshold_chi2: 10.0
+
+fitting:
+  savepseudodata: False
+  fitbasis: EVOL
+  basis:
+  - {fl: sng, trainable: false, smallx: [1.094, 1.118], largex: [1.46, 3.003]}
+  - {fl: g, trainable: false, smallx: [0.8189, 1.044], largex: [2.791, 5.697]}
+  - {fl: v, trainable: false, smallx: [0.457, 0.7326], largex: [1.56, 3.431]}
+  - {fl: v3, trainable: false, smallx: [0.1462, 0.4061], largex: [1.745, 3.452]}
+  - {fl: v8, trainable: false, smallx: [0.5401, 0.7665], largex: [1.539, 3.393]}
+  - {fl: t3, trainable: false, smallx: [-0.4401, 0.9163], largex: [1.773, 3.333]}
+  - {fl: t8, trainable: false, smallx: [0.5852, 0.8537], largex: [1.533, 3.436]}
+  - {fl: t15, trainable: false, smallx: [1.082, 1.142], largex: [1.461, 3.1]}
+
+############################################################
+positivity:
+  posdatasets:
+  - {dataset: NNPDF_POS_2P24GEV_F2U, maxlambda: 1e6}
+  - {dataset: NNPDF_POS_2P24GEV_DYS, maxlambda: 1e5}
+
+integrability:
+  integdatasets:
+  - {dataset: NNPDF_INTEG_3GEV_XT8, maxlambda: 1e2}
+
+############################################################
+debug: true
+double_precision: false
diff --git a/extra_tests/regression_fits/t0theoryid_100.exportgrid b/extra_tests/regression_fits/t0theoryid_100.exportgrid
new file mode 100644
index 0000000000..9f3bddf877
--- /dev/null
+++ b/extra_tests/regression_fits/t0theoryid_100.exportgrid
@@ -0,0 +1,633 @@
+labels: [TBAR, BBAR, CBAR, SBAR, UBAR, DBAR, GLUON, D, U, S, C, B, T, PHT]
+pdfgrid:
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+  -0.011970538645982742, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.010986780747771263, -0.024348648265004158, 0.016582554206252098, -0.0032995790243148804,
+  -0.017741315066814423, -0.0011310994159430265, 0.020982446148991585, 0.03076869435608387,
+  -0.010986778885126114, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.010041953064501286, -0.022123361006379128, 0.014769796282052994, -0.0028656511567533016,
+  -0.014823651872575283, -0.000954114948399365, 0.018559889867901802, 0.027997184544801712,
+  -0.010041952133178711, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.009136131033301353, -0.01998995617032051, 0.013067422434687614, -0.0024663179647177458,
+  -0.012253392487764359, -0.0007820397731848061, 0.01631808839738369, 0.025345182046294212,
+  -0.009136131033301353, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.00826941430568695, -0.017951540648937225, 0.011475360952317715, -0.0021012083161622286,
+  -0.010008588433265686, -0.0006171087152324617, 0.014251683838665485, 0.022814039140939713,
+  -0.00826941430568695, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.007441920693963766, -0.016011040657758713, 0.009993353858590126, -0.001769826514646411,
+  -0.008066674694418907, -0.00046141340862959623, 0.012355155311524868, 0.020405102521181107,
+  -0.007441921625286341, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.006653803866356611, -0.014171098358929157, 0.008620926178991795, -0.001471534138545394,
+  -0.006404568906873465, -0.00031684295390732586, 0.010622823610901833, 0.01811971329152584,
+  -0.006653803866356611, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0059052640572190285, -0.012434182688593864, 0.00735741900280118, -0.001205555396154523,
+  -0.004998939111828804, -0.00018512181122787297, 0.009048879146575928, 0.01595928892493248,
+  -0.0059052640572190285, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.005196551326662302, -0.0108025586232543, 0.006201949901878834, -0.0009709506412036717,
+  -0.003826297353953123, -6.778395618312061e-05, 0.0076273540034890175, 0.013925277628004551,
+  -0.005196552257984877, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.00452797906473279, -0.009278292767703533, 0.0051533835940063, -0.0007666299934498966,
+  -0.0028632059693336487, 3.383122384548187e-05, 0.0063520995900034904, 0.012019170448184013,
+  -0.00452797906473279, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0038999454118311405, -0.00786331295967102, 0.0042103431187570095,
+  -0.0005913350614719093, -0.0020864573307335377, 0.00011860967060783878, 0.005216835532337427,
+  0.010242605581879616, -0.0038999454118311405, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0033129367511719465, -0.006559369619935751, 0.0033711574506014585,
+  -0.00044362491462379694, -0.0014732374111190438, 0.00018567389633972198, 0.004215089604258537,
+  0.008597295731306076, -0.003312937216833234, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0027675670571625233, -0.00536810839548707, 0.002633841009810567, -0.00032186819589696825,
+  -0.0010013276478275657, 0.0002344107924727723, 0.003340221708640456, 0.007085139397531748,
+  -0.0027675670571625233, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0022646018769592047, -0.00429107341915369, 0.0019960568752139807,
+  -0.00022422247275244445, -0.0006492879474535584, 0.0002645174681674689, 0.002585410140454769,
+  0.005708244629204273, -0.002264602342620492, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0018050092039629817, -0.0033297326881438494, 0.0014550576452165842,
+  -0.00014861332601867616, -0.00039665235090069473, 0.0002760429633781314, 0.001943626324646175,
+  0.004468970466405153, -0.0018050092039629817, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0013900256017223, -0.002485557459294796, 0.0010076261823996902, -9.270917507819831e-05,
+  -0.00022414050181396306, 0.0002694795548450202, 0.001407658215612173, 0.0033700650092214346,
+  -0.0013900253688916564, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0010212487541139126, -0.0017600522842258215, 0.0006499586161226034,
+  -5.38890490133781e-05, -0.00011386710684746504, 0.00024587276857346296, 0.0009700758964754641,
+  0.0024147441145032644, -0.0010212487541139126, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0007008198881521821, -0.0011548988986760378, 0.00037753599463030696,
+  -2.9199329219409265e-05, -4.958230420015752e-05, 0.00020701464382000268, 0.00062327750492841,
+  0.0016069578705355525, -0.0007008200045675039, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0004317103303037584, -0.0006721332902088761, 0.0001848672836786136,
+  -1.530605004518293e-05, -1.692245677986648e-05, 0.00015577914018649608, 0.00035953198675997555,
+  0.0009517426951788366, -0.0004317103303037584, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -0.0002183369069825858, -0.0003145375521853566, 6.50491492706351e-05,
+  -8.442447324341629e-06, -3.70333486898744e-06, 9.67963133007288e-05, 0.0001711884542601183,
+  0.00045599686563946307, -0.00021833687787875533, 0.0, 0.0, 0.0]
+- [0.0, 0.0, -6.818278052378446e-05, -8.668409282108769e-05, 8.755068847676739e-06,
+  -4.435501068655867e-06, -2.73809121154045e-07, 3.817993274424225e-05, 5.1455586799420416e-05,
+  0.00013051550195086747, -6.818278052378446e-05, 0.0, 0.0, 0.0]
+- [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+q20: 2.7224999999999997
+replica: 100
+xgrid: [1e-09, 1.29708482343957e-09, 1.68242903474257e-09, 2.18225315420583e-09, 2.83056741739819e-09,
+  3.67148597892941e-09, 4.76222862935315e-09, 6.1770142737618e-09, 8.01211109898438e-09,
+  1.03923870607245e-08, 1.34798064073805e-08, 1.74844503691778e-08, 2.26788118881103e-08,
+  2.94163370300835e-08, 3.81554746595878e-08, 4.94908707232129e-08, 6.41938295708371e-08,
+  8.32647951986859e-08, 1.08001422993829e-07, 1.4008687308113e-07, 1.81704331793772e-07,
+  2.35685551545377e-07, 3.05703512595323e-07, 3.96522309841747e-07, 5.1432125723657e-07,
+  6.67115245136676e-07, 8.65299922973143e-07, 1.12235875241487e-06, 1.45577995547683e-06,
+  1.88824560514613e-06, 2.44917352454946e-06, 3.17671650028717e-06, 4.12035415232797e-06,
+  5.3442526575209e-06, 6.93161897806315e-06, 8.99034258238145e-06, 1.16603030112258e-05,
+  1.51228312288769e-05, 1.96129529349212e-05, 2.54352207134502e-05, 3.29841683435992e-05,
+  4.27707053972016e-05, 5.54561248105849e-05, 7.18958313632514e-05, 9.31954227979614e-05,
+  0.00012078236773133, 0.000156497209466554, 0.000202708936328495, 0.000262459799331951,
+  0.000339645244168985, 0.000439234443000422, 0.000567535660104533, 0.000732507615725537,
+  0.000944112105452451, 0.00121469317686978, 0.00155935306118224, 0.00199627451141338,
+  0.00254691493736552, 0.00323597510213126, 0.00409103436509565, 0.00514175977083962,
+  0.00641865096062317, 0.00795137940306351, 0.009766899996241, 0.0118876139251364,
+  0.0143298947643919, 0.0171032279460271, 0.0202100733925079, 0.0236463971369542,
+  0.0274026915728357, 0.0314652506132444, 0.0358174829282429, 0.0404411060163317,
+  0.0453171343973807, 0.0504266347950069, 0.0557512610084339, 0.0612736019390519,
+  0.0669773829498255, 0.0728475589986517, 0.0788703322292727, 0.0850331197801452,
+  0.0913244910278679, 0.0977340879783772, 0.104252538208639, 0.110871366547237, 0.117582909372878,
+  0.124380233801599, 0.131257062945031, 0.138207707707289, 0.145227005135651, 0.152310263065985,
+  0.159453210652156, 0.166651954293987, 0.173902938455578, 0.181202910873333, 0.188548891679097,
+  0.195938145999193, 0.203368159629765, 0.210836617429103, 0.218341384106561, 0.225880487124065,
+  0.233452101459503, 0.241054536011681, 0.248686221452762, 0.256345699358723, 0.264031612468684,
+  0.271742695942783, 0.279477769504149, 0.287235730364833, 0.295015546847664, 0.302816252626866,
+  0.310636941519503, 0.318476762768082, 0.326334916761672, 0.334210651149156, 0.342103257303627,
+  0.350012067101685, 0.357936449985571, 0.365875810279643, 0.373829584735962, 0.381797240286494,
+  0.389778271981947, 0.397772201099286, 0.40577857340234, 0.413796957540671, 0.421826943574548,
+  0.429868141614175, 0.437920180563205, 0.44598270695699, 0.454055383887562, 0.462137890007651,
+  0.470229918607142, 0.478331176755675, 0.486441384506059, 0.494560274153348, 0.502687589545177,
+  0.510823085439086, 0.518966526903235, 0.527117688756998, 0.535276355048428, 0.543442318565661,
+  0.551615380379768, 0.559795349416641, 0.5679820420558, 0.576175281754088, 0.584374898692498,
+  0.59258072944444, 0.60079261666395, 0.609010408792398, 0.61723395978245, 0.625463128838069,
+  0.633697780169485, 0.641937782762089, 0.650183010158361, 0.658433340251944, 0.666688655093089,
+  0.674948840704708, 0.683213786908386, 0.691483387159697, 0.699757538392251, 0.708036140869916,
+  0.716319098046733, 0.724606316434025, 0.732897705474271, 0.741193177421404, 0.749492647227008,
+  0.757796032432224, 0.766103253064927, 0.774414231541921, 0.782728892575836, 0.791047163086478,
+  0.799368972116378, 0.807694250750291, 0.816022932038457, 0.824354950923382, 0.832690244169987,
+  0.841028750298844, 0.8493704095226, 0.857715163684985, 0.866062956202683, 0.874413732009721,
+  0.882767437504206, 0.891124020497459, 0.899483430165226, 0.907845617001021, 0.916210532771399,
+  0.924578130473112, 0.932948364292029, 0.941321189563734, 0.949696562735755, 0.958074441331298,
+  0.966454783914439, 0.974837550056705, 0.983222700304978, 0.991610196150662, 1.0]
diff --git a/extra_tests/regression_fits/t0theoryid_100.json b/extra_tests/regression_fits/t0theoryid_100.json
new file mode 100644
index 0000000000..e64345481c
--- /dev/null
+++ b/extra_tests/regression_fits/t0theoryid_100.json
@@ -0,0 +1,95 @@
+{
+  "preprocessing": [
+    {
+      "fl": "sng",
+      "smallx": 1.1168484687805176,
+      "largex": 1.4932247400283813,
+      "trainable": false
+    },
+    {
+      "fl": "g",
+      "smallx": 0.9923287034034729,
+      "largex": 3.7714877128601074,
+      "trainable": false
+    },
+    {
+      "fl": "v",
+      "smallx": 0.4898352324962616,
+      "largex": 1.672149658203125,
+      "trainable": false
+    },
+    {
+      "fl": "v3",
+      "smallx": 0.28436920046806335,
+      "largex": 3.4038245677948,
+      "trainable": false
+    },
+    {
+      "fl": "v8",
+      "smallx": 0.635401725769043,
+      "largex": 1.9356087446212769,
+      "trainable": false
+    },
+    {
+      "fl": "t3",
+      "smallx": 0.5063296556472778,
+      "largex": 2.4878852367401123,
+      "trainable": false
+    },
+    {
+      "fl": "t8",
+      "smallx": 0.8517391681671143,
+      "largex": 2.7349023818969727,
+      "trainable": false
+    },
+    {
+      "fl": "t15",
+      "smallx": 1.1255686283111572,
+      "largex": 1.68263578414917,
+      "trainable": false
+    }
+  ],
+  "stop_epoch": 1100,
+  "best_epoch": 1099,
+  "erf_tr": 2.978336811065674,
+  "erf_vl": 2.3964874744415283,
+  "chi2": 2.117762327194214,
+  "pos_state": "POS_VETO",
+  "arc_lengths": [
+    1.313367640087446,
+    1.5272293101266203,
+    1.1740515448639215,
+    1.0004956501751476,
+    1.2430530730062526
+  ],
+  "integrability": [
+    0.0009352681590863443,
+    0.0009352681590831802,
+    5.0388540784207514e-05,
+    0.4628416448831547,
+    0.00719353713793458
+  ],
+  "timing": {
+    "walltime": {
+      "Total": 11.484477519989014,
+      "start": 0.0,
+      "replica_set": 0.22041726112365723,
+      "replica_fitted": 11.484412670135498,
+      "replica_set_to_replica_fitted": 11.26399540901184
+    },
+    "cputime": {
+      "Total": 13.09741417,
+      "start": 0.0,
+      "replica_set": 0.21945147399999954,
+      "replica_fitted": 13.097347254999999,
+      "replica_set_to_replica_fitted": 12.877895781
+    }
+  },
+  "version": {
+    "keras": "3.6.0",
+    "tensorflow": "2.18.0, mkl=False",
+    "numpy": "1.26.4",
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
+  }
+}
diff --git a/extra_tests/regression_fits/trainable_prepro_61.json b/extra_tests/regression_fits/trainable_prepro_61.json
index 52aec8f19f..6c5f497b69 100644
--- a/extra_tests/regression_fits/trainable_prepro_61.json
+++ b/extra_tests/regression_fits/trainable_prepro_61.json
@@ -71,25 +71,25 @@
   ],
   "timing": {
     "walltime": {
-      "Total": 12.690647840499878,
+      "Total": 12.306910037994385,
       "start": 0.0,
-      "replica_set": 0.23492431640625,
-      "replica_fitted": 12.690513372421265,
-      "replica_set_to_replica_fitted": 12.455589056015015
+      "replica_set": 0.23054814338684082,
+      "replica_fitted": 12.30685043334961,
+      "replica_set_to_replica_fitted": 12.076302289962769
     },
     "cputime": {
-      "Total": 14.288013259,
+      "Total": 13.912358415,
       "start": 0.0,
-      "replica_set": 0.23258636100000007,
-      "replica_fitted": 14.287876923999999,
-      "replica_set_to_replica_fitted": 14.055290563
+      "replica_set": 0.22952914600000085,
+      "replica_fitted": 13.912296499,
+      "replica_set_to_replica_fitted": 13.682767353
     }
   },
   "version": {
     "keras": "3.6.0",
-    "tensorflow": "2.17.0, mkl=False",
+    "tensorflow": "2.18.0, mkl=False",
     "numpy": "1.26.4",
-    "nnpdf": "4.0.9.post1366.dev0+f934deb20",
-    "validphys": "4.0.9.post1366.dev0+f934deb20"
+    "nnpdf": "4.0.9.post1608.dev0+77c3343e3",
+    "validphys": "4.0.9.post1608.dev0+77c3343e3"
   }
 }
diff --git a/validphys2/src/validphys/config.py b/validphys2/src/validphys/config.py
index 6b520b8906..475a22bfda 100644
--- a/validphys2/src/validphys/config.py
+++ b/validphys2/src/validphys/config.py
@@ -167,7 +167,7 @@ def parse_unpolarized_bc(self, name):
     @_id_with_label
     def parse_theoryid(self, theoryID: (str, int)):
         """A number corresponding to the database theory ID where the
-        corresponding theory folder is installed in te data directory."""
+        corresponding theory folder is installed in the data directory."""
         try:
             return self.loader.check_theoryID(theoryID)
         except LoaderError as e:
@@ -642,7 +642,6 @@ def produce_dataset(
         frac = dataset_input.frac
         weight = dataset_input.weight
         variant = dataset_input.variant
-
         try:
             ds = self.loader.check_dataset(
                 name=name,
@@ -661,7 +660,6 @@ def produce_dataset(
 
         except LoadFailedError as e:
             raise ConfigError(e)
-
         if check_plotting:
             # normalize=True should check for more stuff
             get_info(ds, normalize=True)
@@ -669,6 +667,30 @@ def produce_dataset(
                 log.warning(f"Plotting files not found for: {ds}")
         return ds
 
+    def produce_t0dataset(
+        self,
+        *,
+        dataset_input,
+        t0id,
+        cuts,
+        use_fitcommondata=False,
+        fit=None,
+        check_plotting: bool = False,
+    ):
+        """
+        Same as produce_dataset, but if a ``t0theoryid`` has been defined in the
+        runcard then those corresponding fktables will be linked.
+        """
+        ds = self.produce_dataset(
+            dataset_input=dataset_input,
+            theoryid=t0id,
+            cuts=cuts,
+            use_fitcommondata=use_fitcommondata,
+            fit=fit,
+            check_plotting=check_plotting,
+        )
+        return ds
+
     @configparser.element_of("experiments")
     def parse_experiment(self, experiment: dict):
         """A set of datasets where correlated systematics are taken
@@ -991,7 +1013,6 @@ def parse_use_t0(self, do_use_t0: bool):
         """Whether to use the t0 PDF set to generate covariance matrices."""
         return do_use_t0
 
-    # TODO: Find a good name for this
     def produce_t0set(self, t0pdfset=None, use_t0=False):
         """Return the t0set if use_t0 is True and None otherwise. Raises an
         error if t0 is requested but no t0set is given.
@@ -1002,6 +1023,28 @@ def produce_t0set(self, t0pdfset=None, use_t0=False):
             return t0pdfset
         return None
 
+    def parse_t0theoryid(self, theoryID: (str, int)):
+        """A number corresponding to the database theory ID where the
+        corresponding theory folder is installed in te data directory.
+
+        The t0theoryid is specifically used for SM parameter determinatins (e.g.
+        alphas) using the correlated replicas method of arXiv: 1802.03398. To do
+        an alphas determination we perform multiple fits, each with a different
+        value of alphas in the DGLAP kernel and hard scattering cross section.
+        Then we compute the chi2 for each fit to determine which alphas best
+        describes the data, however, to make a fair comparison we need to ensure
+        that the chi2 (and thus the t0 covariance matrix) has to be exactly the
+        same for each fit. This requires not only to fix the t0pdfset between
+        the different fits, but also to fix the t0theoryid.
+        """
+        return self.parse_theoryid(theoryID)
+
+    def produce_t0id(self, theoryid, t0theoryid=None):
+        """Return the t0id if t0theoryid is set and return theoryid otherwise."""
+        if t0theoryid:
+            theoryid = t0theoryid
+        return theoryid
+
     def parse_luxset(self, name):
         """PDF set used to generate the photon with fiatlux."""
         return self.parse_pdf(name)
@@ -1628,13 +1671,12 @@ def produce_group_dataset_inputs_by_experiment(self, data_input):
     def produce_group_dataset_inputs_by_process(self, data_input):
         return self.produce_group_dataset_inputs_by_metadata(data_input, "nnpdf31_process")
 
-    def produce_scale_variation_theories(self, theoryid, point_prescription):
+    def produce_scale_variation_theories(self, point_prescription, t0id):
         """Produces a list of theoryids given a theoryid at central scales and a point
-        prescription. The options for the latter are '3 point', '5 point', '5bar point', '7 point'
-        and '9 point'. Note that these are defined in arXiv:1906.10698. This hard codes the
-        theories needed for each prescription to avoid user error."""
+        prescription. The options for the latter are defined in pointprescriptions.yaml.
+        This hard codes the theories needed for each prescription to avoid user error."""
         pp = point_prescription
-        th = theoryid.id
+        th = t0id.id
 
         lsv = yaml.safe_load(read_text(validphys.scalevariations, "scalevariationtheoryids.yaml"))
 
diff --git a/validphys2/src/validphys/covmats.py b/validphys2/src/validphys/covmats.py
index dec41cd2ba..e280531368 100644
--- a/validphys2/src/validphys/covmats.py
+++ b/validphys2/src/validphys/covmats.py
@@ -224,7 +224,7 @@ def dataset_inputs_covmat_from_systematics(
 
 @check_cuts_considered
 @functools.lru_cache
-def dataset_t0_predictions(dataset, t0set):
+def dataset_t0_predictions(t0dataset, t0set):
     """Returns the t0 predictions for a ``dataset`` which are the predictions
     calculated using the central member of ``pdf``. Note that if ``pdf`` has
     errortype ``replicas``, and the dataset is a hadronic observable then the
@@ -246,7 +246,7 @@ def dataset_t0_predictions(dataset, t0set):
     """
     # reshape because the underlying data has shape ndata * 1
     # accounting for the fact that some datasets are single datapoint
-    return central_predictions(dataset, t0set).to_numpy().reshape(-1)
+    return central_predictions(t0dataset, t0set).to_numpy().reshape(-1)
 
 
 def t0_covmat_from_systematics(
diff --git a/validphys2/src/validphys/pseudodata.py b/validphys2/src/validphys/pseudodata.py
index 05be16807e..f850fa2ea5 100644
--- a/validphys2/src/validphys/pseudodata.py
+++ b/validphys2/src/validphys/pseudodata.py
@@ -322,7 +322,7 @@ def level0_commondata_wc(data, fakepdf):
         # == Generate a new CommonData instance with central value given by Level 0 data generated with fakepdf ==#
 
         t0_prediction = dataset_t0_predictions(
-            dataset=dataset, t0set=fakepdf
+            t0dataset=dataset, t0set=fakepdf
         )  # N.B. cuts already applied to th. pred.
         level0_commondata_instances_wc.append(commondata_wc.with_central_value(t0_prediction))
 
diff --git a/validphys2/src/validphys/tests/test_covmats.py b/validphys2/src/validphys/tests/test_covmats.py
index 3148ad1f40..cb1dc0a1bf 100644
--- a/validphys2/src/validphys/tests/test_covmats.py
+++ b/validphys2/src/validphys/tests/test_covmats.py
@@ -144,9 +144,9 @@ def test_systematic_matrix(data_config, use_cuts, dataset_input):
 
 def test_single_datapoint(single_data_single_point_internal_cuts_config):
     # Make the t0 predictions
-    ds = API.dataset(**single_data_single_point_internal_cuts_config)
+    t0ds = API.t0dataset(**single_data_single_point_internal_cuts_config)
     t0set = API.pdf(**single_data_single_point_internal_cuts_config)
-    t0_predictions = dataset_t0_predictions(ds, t0set)
+    t0_predictions = dataset_t0_predictions(t0ds, t0set)
 
     cd = API.commondata(**single_data_single_point_internal_cuts_config)
     ld = load_commondata(cd)
diff --git a/validphys2/src/validphys/tests/test_pseudodata.py b/validphys2/src/validphys/tests/test_pseudodata.py
index 2b0599853d..21de5fd0a3 100644
--- a/validphys2/src/validphys/tests/test_pseudodata.py
+++ b/validphys2/src/validphys/tests/test_pseudodata.py
@@ -103,5 +103,5 @@ def test_level0_commondata_wc():
     )
     l0_vals = l0_cd[0].central_values
     assert_allclose(
-        dataset_t0_predictions(dataset=datasetspec, t0set=t0set), l0_vals, rtol=1e-07, atol=0
+        dataset_t0_predictions(t0dataset=datasetspec, t0set=t0set), l0_vals, rtol=1e-07, atol=0
     )
diff --git a/validphys2/src/validphys/tests/test_pythonmakereplica.py b/validphys2/src/validphys/tests/test_pythonmakereplica.py
index 217355fb40..a07a363aed 100644
--- a/validphys2/src/validphys/tests/test_pythonmakereplica.py
+++ b/validphys2/src/validphys/tests/test_pythonmakereplica.py
@@ -49,6 +49,7 @@ def test_commondata_unchanged(data_config, dataset_inputs, use_cuts):
     pre_mkrep_cvs = [deepcopy(cd.central_values) for cd in ld_cds]
     pre_mkrep_sys_tabs = [deepcopy(cd.systematics_table) for cd in ld_cds]
     pre_mkrep_cd_tabs = [deepcopy(cd.commondata_table) for cd in ld_cds]
+
     API.make_replica(**config)
 
     for post_mkrep_cd, pre_mkrep_cv in zip(ld_cds, pre_mkrep_cvs):