{"id":"W2973477763","doi":"10.21926/obm.genet.1903094","title":"Introduction to Genetic Screening","year":2019,"lang":"en","type":"article","venue":"OBM Genetics","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Perspective (graphical); Field (mathematics); Engineering ethics; Data science; Computational biology; Computer science; Biology; Engineering; Artificial intelligence; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008742011,0.0001891676,0.0001356773,0.00007447739,0.00006910437,0.0000435896,0.0002227525,0.000128497,0.000248917],"category_scores_gemma":[0.0000431298,0.0002145183,0.00009223645,0.0001361437,0.00003690135,0.000002261736,0.0001353313,0.00006146159,0.0003729797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001293207,"about_ca_system_score_gemma":0.00005522175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004723338,"about_ca_topic_score_gemma":0.000009819804,"domain_scores_codex":[0.9986312,0.00004461552,0.0002259856,0.000567139,0.000195915,0.0003351248],"domain_scores_gemma":[0.9988527,0.000004703999,0.00005171253,0.0007042572,0.0001434376,0.0002432453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001888525,0.0001312792,0.04467195,0.00005077663,0.0000561074,0.000003054092,0.00006155834,0.006149072,0.8761989,0.00004703367,0.05613942,0.01630194],"study_design_scores_gemma":[0.001008175,0.0007165514,0.06507918,0.00001320516,0.00004500768,0.00002175298,0.0001052796,0.0002388806,0.2333559,0.0001533687,0.6987377,0.000524924],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857606,0.001849782,0.009600142,0.0006757217,0.0006960481,0.0003925595,0.00002591594,0.00002585727,0.0009733153],"genre_scores_gemma":[0.9649851,0.0004463959,0.02503133,0.001126525,0.003799753,0.00002968413,0.0001821479,0.00005932949,0.004339699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6428431,"threshold_uncertainty_score":0.8747801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005678042389443491,"score_gpt":0.2264695134500616,"score_spread":0.2207914710606181,"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."}}