{"id":"W3023536793","doi":"10.1016/j.forsciint.2020.110314","title":"Building a ground-truth fingerprint dataset for proficiency testing and research","year":2020,"lang":"en","type":"article","venue":"Forensic Science International","topic":"Forensic Fingerprint Detection Methods","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; Royal Canadian Mounted Police","funders":"","keywords":"Computer science; Workflow; Fingerprint (computing); Identification (biology); Process (computing); Crime scene; Quality (philosophy); ENCODE; Variety (cybernetics); Artificial intelligence; Database; Psychology; Biology","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":[0.001705304,0.001207888,0.0009971757,0.004515375,0.0007895431,0.001341836,0.001961984,0.001783617,0.006630489],"category_scores_gemma":[0.006042446,0.0004296553,0.0009343129,0.002581476,0.0004899718,0.001633355,0.002274921,0.001108901,0.006807046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007145787,"about_ca_system_score_gemma":0.002189608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008075983,"about_ca_topic_score_gemma":0.0108209,"domain_scores_codex":[0.9977641,0.0002721059,0.00017537,0.0007447912,0.0007581974,0.0002855482],"domain_scores_gemma":[0.9958072,0.0006684859,0.0003325335,0.001533858,0.001452308,0.0002057234],"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":[0.0006686108,0.001636244,0.05869943,0.001250263,0.0003477818,0.001049624,0.0002499995,0.02119356,0.05248725,0.004159988,0.1859233,0.6723339],"study_design_scores_gemma":[0.0003099181,0.001255376,0.1648885,0.0009492561,0.0005392413,0.005164009,0.001857292,0.3557374,0.1205811,0.02484633,0.3235753,0.000296331],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.1984062,0.001794307,0.4759693,0.0009923745,0.0005626779,0.001949393,0.2828251,0.02237951,0.01512107],"genre_scores_gemma":[0.22438,0.0005965725,0.2539911,0.00038987,0.0001031757,0.001218942,0.5124866,0.0004985675,0.006335245],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.008075983,"threshold_uncertainty_score":0.02218115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2447487091095955,"score_gpt":0.4854128754507934,"score_spread":0.2406641663411979,"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."}}