{"id":"W4392616493","doi":"10.1016/j.gimo.2024.100909","title":"P032: Decoding the genetic tapestry of long chain fatty acid oxidation disorders: Unveiling novel insights with a dynamic locus-specific gene database","year":2024,"lang":"en","type":"article","venue":"Genetics in Medicine Open","topic":"Metabolism and Genetic Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Locus (genetics); Gene; Genetics; Decoding methods; Computational biology; Biology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002324069,0.0007669952,0.001080982,0.001828664,0.0006979882,0.003550153,0.001754912,0.001051789,0.005175808],"category_scores_gemma":[0.009176786,0.0004438199,0.0006111029,0.002240688,0.0006797441,0.003322873,0.002364309,0.001445838,0.005161682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005650715,"about_ca_system_score_gemma":0.002147108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002257947,"about_ca_topic_score_gemma":0.00322655,"domain_scores_codex":[0.9986634,0.0002849614,0.00012349,0.0004887267,0.0003496791,0.00008983585],"domain_scores_gemma":[0.9972863,0.001007388,0.0002151007,0.0006595234,0.0005340387,0.0002976671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002393868,0.0002607635,0.03735723,0.002190467,0.0004454881,0.003883177,0.001018884,0.01104527,0.08106821,0.04654543,0.2447604,0.5690308],"study_design_scores_gemma":[0.0003657945,0.0005730193,0.01273631,0.0008552109,0.000486484,0.008000276,0.001321271,0.1359821,0.05791492,0.1200466,0.6614786,0.0002393788],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1520423,0.01707421,0.6114506,0.02126328,0.002830931,0.0003058744,0.1210057,0.04486723,0.02915987],"genre_scores_gemma":[0.3190032,0.01151824,0.4081488,0.003193811,0.001000595,0.0003374629,0.2431021,0.005679942,0.008015986],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005175808,"threshold_uncertainty_score":0.01731479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01722324272786699,"score_gpt":0.2902880919507704,"score_spread":0.2730648492229034,"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."}}