{"id":"W4402652826","doi":"10.1530/endoabs.104.oc4","title":"Selective sweep mapping identifies obesity candidate mutations in labrador retrievers","year":2024,"lang":"en","type":"article","venue":"Endocrine Abstracts","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Geography; Genetics; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004017632,0.0007785611,0.0005262679,0.002721176,0.0006832223,0.0008428142,0.00115224,0.00116732,0.005337689],"category_scores_gemma":[0.001298547,0.0002006304,0.0009558967,0.001431663,0.0005945748,0.0002092928,0.0004555341,0.0005319252,0.001296059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004173623,"about_ca_system_score_gemma":0.0002933599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00666149,"about_ca_topic_score_gemma":0.009414977,"domain_scores_codex":[0.9995233,0.00004918331,0.00005908197,0.0001743084,0.0001068136,0.00008736505],"domain_scores_gemma":[0.9994305,0.0001867527,0.0002029178,0.00004812907,0.00004842571,0.00008339498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.005383986,0.0006849458,0.4082733,0.0004720849,0.001708814,0.03941455,0.001658139,0.001485643,0.4675599,0.001365806,0.003155857,0.06883696],"study_design_scores_gemma":[0.0003914447,0.0006996182,0.9003076,0.0002607728,0.001589542,0.04737321,0.001225183,0.003987084,0.03177784,0.0008386935,0.01143296,0.0001158664],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953619,0.0004494433,0.0009179374,0.0001637503,0.00003005258,0.00003604954,0.001377115,0.00009364577,0.001570176],"genre_scores_gemma":[0.9963596,0.0002553637,0.0008388311,0.0001335096,0.00003507629,0.00001118555,0.0009337396,0.00008476266,0.001347841],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00666149,"threshold_uncertainty_score":0.01785636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006884435566952463,"score_gpt":0.2628412982508177,"score_spread":0.2559568626838652,"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."}}