{"id":"W2322288753","doi":"10.1080/00085030.2009.10757601","title":"Lifting Fingerprints from Skin Using Silicone","year":2009,"lang":"en","type":"article","venue":"Canadian Society of Forensic Science Journal","topic":"Forensic Fingerprint Detection Methods","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Silicone; Fingerprint (computing); Lift (data mining); Materials science; Computer science; Biomedical engineering; Pattern recognition (psychology); Artificial intelligence; Composite material; Data mining; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0007094402,0.0004961974,0.0003024597,0.0007355185,0.0003275353,0.0003206723,0.0003920275,0.0004249867,0.004291932],"category_scores_gemma":[0.001312304,0.0002557334,0.0005788504,0.000352647,0.0002979746,0.0005638159,0.0005943179,0.0004896566,0.001393943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001151345,"about_ca_system_score_gemma":0.0001677674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000280824,"about_ca_topic_score_gemma":0.0004311352,"domain_scores_codex":[0.9995085,0.00006196507,0.00002729576,0.0000735657,0.0002670431,0.00006163291],"domain_scores_gemma":[0.9993099,0.0002917567,0.00009113503,0.000108061,0.0001461285,0.00005302659],"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":[0.0002612052,0.00005013132,0.001635137,0.0002833775,0.00001879227,0.0003238606,0.0001442398,0.0001385986,0.9179904,0.0001796933,0.0003512739,0.07862326],"study_design_scores_gemma":[0.00002478932,0.001550005,0.01577232,0.0001120356,0.00005676826,0.003686099,0.0002689794,0.001583665,0.9680158,0.0002630802,0.008614206,0.00005226696],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8194947,0.005798484,0.1615843,0.0005974232,0.0004193432,0.0004066813,0.0002574367,0.0006934882,0.01074814],"genre_scores_gemma":[0.8422912,0.003644035,0.140485,0.0004464592,0.0001061992,0.0001104381,0.0002892827,0.0001071446,0.01252015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004291932,"threshold_uncertainty_score":0.01435792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03814972443174112,"score_gpt":0.3341316220403995,"score_spread":0.2959818976086583,"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."}}