{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001816496,0.0001404415,0.0001131536,0.0001407747,0.00006586427,0.00009114502,0.0001330072,0.00006121219,0.00004251446],"category_scores_gemma":[0.000332795,0.0001434831,0.00005589507,0.0002743496,0.00004405767,0.00001947379,0.00006224604,0.000289695,0.00009310114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004180525,"about_ca_system_score_gemma":0.00009783274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005355193,"about_ca_topic_score_gemma":0.0005906341,"domain_scores_codex":[0.9990329,0.00003349962,0.0002910581,0.000240944,0.0001320705,0.0002695915],"domain_scores_gemma":[0.9996046,0.00004042072,0.0000598189,0.0001929081,0.0000441712,0.00005807598],"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":[0.0002195209,0.000492334,0.05300872,0.001960771,0.0008917413,0.001258862,0.009807432,0.0361674,0.8083162,0.001749074,0.05858662,0.02754138],"study_design_scores_gemma":[0.0009511038,0.0001859628,0.5016533,0.000234142,0.00004049654,0.0002248081,0.001318086,0.003491424,0.3792159,0.0006640003,0.1112771,0.0007436913],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892491,0.0003068054,0.0003377437,0.000279627,0.0002969941,0.0001622116,0.00002207508,0.00005752758,0.009287965],"genre_scores_gemma":[0.9963867,0.00007129556,0.0007751207,0.00007799453,0.0001317009,0.00001246761,0.0002262959,0.00001929165,0.002299141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4486446,"threshold_uncertainty_score":0.5851071,"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."}}