{"id":"W3033343774","doi":"10.1534/g3.120.401244","title":"Bayesian and Machine Learning Models for Genomic Prediction of Anterior Cruciate Ligament Rupture in the Canine Model","year":2020,"lang":"en","type":"article","venue":"G3 Genes Genomes Genetics","topic":"Veterinary Orthopedics and Neurology","field":"Veterinary","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"Anterior cruciate ligament; Genome-wide association study; Machine learning; Medicine; Labrador Retriever; Artificial intelligence; Single-nucleotide polymorphism; Bioinformatics; Computer science; Biology; Surgery; Genetics; Genotype; Gene","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":[0.0002365747,0.0002155243,0.0003631524,0.00007054548,0.00009750339,0.0000240798,0.0002387115,0.0001092121,0.000007985921],"category_scores_gemma":[0.000003988428,0.0001818559,0.00009494953,0.0001043114,0.00005830051,0.00004166016,0.0001880776,0.0001787381,8.806157e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001693299,"about_ca_system_score_gemma":0.00004282431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001956051,"about_ca_topic_score_gemma":0.00002296769,"domain_scores_codex":[0.9985781,0.0001050527,0.0005061188,0.0003865321,0.0001352341,0.0002889908],"domain_scores_gemma":[0.9994339,0.00003871003,0.0001807833,0.000222102,0.00003901211,0.00008543573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001096967,0.0001014891,0.01217039,0.0003785826,0.00008617431,0.00008341885,0.01102222,0.4057669,0.2661064,0.00003441789,0.00001932772,0.3031338],"study_design_scores_gemma":[0.0008045477,0.003427901,0.001518502,0.00002425709,0.00005428569,0.0001351361,0.0001691516,0.9895569,0.0001844214,0.0002950575,0.003671375,0.0001584424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7652555,0.2154582,0.01762401,0.0007597737,0.00006860799,0.0005687058,0.0001918954,0.00002328058,0.00005001429],"genre_scores_gemma":[0.8972899,0.09910263,0.002630495,0.0007169807,0.0001232384,0.0000582135,0.00001588759,0.00004168885,0.000020976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5837901,"threshold_uncertainty_score":0.741587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0984324839175072,"score_gpt":0.2798520308730672,"score_spread":0.18141954695556,"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."}}