{"id":"W4413228409","doi":"10.32942/x26m0p","title":"Predictive Evolutionary Genomics: Principles, Validation, and Practice","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Howard Hughes Medical Institute","keywords":"Inference; Probabilistic logic; Genetic architecture; Computer science; Bayesian probability; Selection (genetic algorithm); Bayesian inference; Machine learning; Data science; Artificial intelligence; Quantitative trait locus; Biology; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06584787,0.001910502,0.002055168,0.003897638,0.001023163,0.008738591,0.004378481,0.003312542,0.001818704],"category_scores_gemma":[0.1291367,0.00111613,0.001215841,0.003252301,0.01254434,0.008726183,0.005239264,0.005093266,0.0006536847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003540977,"about_ca_system_score_gemma":0.004150603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004138584,"about_ca_topic_score_gemma":0.001506636,"domain_scores_codex":[0.9804686,0.01303828,0.000767833,0.00258646,0.002861619,0.0002772379],"domain_scores_gemma":[0.8857638,0.0910373,0.003482841,0.014595,0.004417534,0.0007035854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007781187,0.0001146226,0.008633836,0.0006520351,0.0003514215,0.0001524699,0.001067636,0.192459,0.0008424239,0.6129944,0.002462992,0.1801913],"study_design_scores_gemma":[0.00002532205,0.00003218503,0.0008040384,0.0003848696,0.00003462077,0.00005005,0.0001318434,0.2104918,0.0007018849,0.7821223,0.005183223,0.00003788789],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007152598,0.002088234,0.9808168,0.004645992,0.0000828812,0.0001051676,0.0001409131,0.0004205953,0.00454693],"genre_scores_gemma":[0.3493859,0.004775288,0.6417139,0.001110476,0.0005482123,0.000561767,0.0004281473,0.0004015169,0.001074838],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06584787,"threshold_uncertainty_score":0.348241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02085718969790304,"score_gpt":0.2679654284396376,"score_spread":0.2471082387417346,"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."}}