{"id":"W2802252859","doi":"10.1503/cmaj.180057","title":"Secrets in fingerprints: clinical ambitions and uncertainty in dermatoglyphics","year":2018,"lang":"en","type":"article","venue":"Canadian Medical Association Journal","topic":"Dermatoglyphics and Human Traits","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dermatoglyphics; Biometrics; Identification (biology); Fingerprint (computing); Identity (music); Computer science; Data science; Artificial intelligence; Biology; Genetics; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01160033,0.0003613921,0.000528753,0.004203264,0.002354026,0.004375541,0.0009169814,0.002844143,0.001341127],"category_scores_gemma":[0.06457184,0.0003005671,0.0001801755,0.002838206,0.02177938,0.00750115,0.004116731,0.003250166,0.0001681048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002006216,"about_ca_system_score_gemma":0.001742374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002225635,"about_ca_topic_score_gemma":0.001729622,"domain_scores_codex":[0.9935402,0.003280659,0.0004923859,0.0005737058,0.001714891,0.0003982229],"domain_scores_gemma":[0.9752722,0.01686109,0.002998712,0.001472618,0.002392976,0.001002266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0009295513,0.0001075065,0.2027813,0.0005342812,0.0001079883,0.01715805,0.0518562,0.001349904,0.004201133,0.4532366,0.01745225,0.2502854],"study_design_scores_gemma":[0.00005064565,0.0001955843,0.07128428,0.0012903,0.00009097649,0.06585915,0.05852848,0.002242899,0.002240072,0.7644343,0.03358272,0.0002006787],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5948322,0.08083436,0.01721014,0.2568344,0.001095222,0.00006691844,0.0004727984,0.00007394204,0.04858005],"genre_scores_gemma":[0.9885215,0.006036175,0.001373872,0.002968348,0.0006402771,0.00001305601,0.00003573494,0.00001472507,0.0003964088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01160033,"threshold_uncertainty_score":0.06134915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0116259905778037,"score_gpt":0.2869119690399837,"score_spread":0.2752859784621799,"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."}}