{"id":"W3010513926","doi":"10.1080/00085030.2020.1736812","title":"Quantity and asymmetry of fingerprint white lines: forensic implication","year":2020,"lang":"en","type":"article","venue":"Canadian Society of Forensic Science Journal","topic":"Dermatoglyphics and Human Traits","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Dermatoglyphics; Digit ratio; Forensic identification; Logistic regression; Hausa; Proxy (statistics); Psychology; Fluctuating asymmetry; Fingerprint (computing); Asymmetry; Numerical digit; Population; Statistics; Mathematics; Demography; Medicine; Computer science; Artificial intelligence; Arithmetic; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003203082,0.00007882,0.0001302299,0.000045136,0.0001896124,0.00004116987,0.0002325157,0.00006791767,0.00000792206],"category_scores_gemma":[0.00007666275,0.00007555191,0.0001085488,0.000218966,0.0007469489,0.00001441007,0.00004930392,0.0001139146,5.157781e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002350482,"about_ca_system_score_gemma":0.0007145071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003880939,"about_ca_topic_score_gemma":0.00178302,"domain_scores_codex":[0.9991698,0.000006944498,0.0002243887,0.0001874607,0.0001900924,0.000221323],"domain_scores_gemma":[0.9989824,0.00000431129,0.0001692238,0.0001299846,0.0003012826,0.000412763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002378535,0.00002675632,0.1287488,0.0001542491,0.00009967036,0.000003153558,0.003164336,0.0001003406,0.7872421,0.002827619,0.03325941,0.04434983],"study_design_scores_gemma":[0.001056068,0.000620432,0.401741,0.0001272891,0.00005683878,0.0002582664,0.002817445,0.002588524,0.5807056,0.001534471,0.008004994,0.0004890739],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964458,0.0003973269,0.0009347528,0.00175511,0.00007674515,0.0000567522,0.00001923687,0.000001802286,0.000312428],"genre_scores_gemma":[0.9908508,0.0001705372,0.007760925,0.001071235,0.0001259482,4.928827e-7,0.000004402192,0.000005958825,0.000009664202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2729923,"threshold_uncertainty_score":0.3080917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174704518994088,"score_gpt":0.2529653256844795,"score_spread":0.2354948737850708,"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."}}