{"id":"W2810952459","doi":"10.1186/s12863-018-0626-7","title":"Multivariate genome-wide association analysis identifies novel and relevant variants associated with anterior cruciate ligament rupture risk in the dog model","year":2018,"lang":"en","type":"article","venue":"BMC Genetics","topic":"Veterinary Orthopedics and Neurology","field":"Veterinary","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institutes of Health; University of Wisconsin-Madison; American Kennel Club Canine Health Foundation; Morris Animal Foundation","keywords":"Multivariate statistics; Anterior cruciate ligament; Multivariate analysis; Association (psychology); Genetic association; Genome-wide association study; Medicine; Biology; Internal medicine; Genetics; Anatomy; Computer science; Single-nucleotide polymorphism; Psychology; Genotype; Gene; Machine learning","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":[0.001254083,0.0002644522,0.0004678829,0.0002472851,0.0002344142,0.0001384784,0.0002736852,0.0002143357,0.00001540833],"category_scores_gemma":[0.0004696708,0.0001950674,0.0001196229,0.0005275615,0.00008846183,0.00008051968,0.0001938193,0.0002856222,0.00001029166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008811255,"about_ca_system_score_gemma":0.00005305184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001295587,"about_ca_topic_score_gemma":0.001354091,"domain_scores_codex":[0.9977052,0.0004047675,0.0005536673,0.0005233686,0.0003731266,0.000439835],"domain_scores_gemma":[0.9983472,0.0003878603,0.0005873089,0.0004535303,0.0001559155,0.00006823806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006735652,0.0004935703,0.957239,0.00003055003,0.001123784,0.0001479824,0.008809173,0.005037656,0.02606932,0.000006066853,0.00003403566,0.000335287],"study_design_scores_gemma":[0.001014009,0.001055318,0.8419011,0.00004105057,0.000606767,0.00001982716,0.00009544694,0.1548112,0.00002476982,0.0001727669,0.00004156938,0.0002162444],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906927,0.0004699977,0.007839732,0.0002013167,0.0001195489,0.0003703859,0.0001774221,0.0000335808,0.00009529608],"genre_scores_gemma":[0.9958258,0.000878502,0.002423796,0.0005443998,0.0000800782,0.00003486902,0.00001486845,0.00003394519,0.0001637393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1497735,"threshold_uncertainty_score":0.7954618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0437770758052898,"score_gpt":0.2971287423357338,"score_spread":0.2533516665304441,"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."}}