{"id":"W4414149220","doi":"10.1016/j.anscip.2025.07.252","title":"5. DMI model hybridization: A bootstrap approach to model performance assessment","year":2025,"lang":"en","type":"article","venue":"Animal - science proceedings","topic":"Ruminant Nutrition and Digestive Physiology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Bayesian probability; Stability (learning theory); Measure (data warehouse); Statistical model","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02588013,0.001454,0.001671156,0.002793133,0.001405975,0.001968721,0.003554116,0.002821225,0.009059219],"category_scores_gemma":[0.09957759,0.0006985422,0.002186935,0.001543384,0.001014745,0.002413354,0.003017986,0.003498257,0.002023268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008045047,"about_ca_system_score_gemma":0.001298505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002884568,"about_ca_topic_score_gemma":0.00485819,"domain_scores_codex":[0.9864554,0.01037567,0.0004939412,0.0009063871,0.00141012,0.0003584668],"domain_scores_gemma":[0.9547272,0.03524941,0.001074649,0.004440121,0.004132382,0.0003763127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0013566,0.0006661295,0.011272,0.0007237956,0.002232298,0.000474431,0.0004621419,0.4506721,0.009874217,0.05155507,0.01553128,0.45518],"study_design_scores_gemma":[0.00004498543,0.0001287694,0.0009923617,0.00004470735,0.00008645958,0.00006864608,0.00004514978,0.9808047,0.002405772,0.01346318,0.00187652,0.00003872871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00860125,0.0001069935,0.9875942,0.0002363681,0.00007117132,0.0001325228,0.0003186521,0.001481286,0.001457475],"genre_scores_gemma":[0.2003565,0.00007553827,0.7953546,0.0003283288,0.000108752,0.0004754828,0.001128368,0.0009261665,0.001246253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02588013,"threshold_uncertainty_score":0.1368688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04347329738235021,"score_gpt":0.2897445801835445,"score_spread":0.2462712828011943,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}