{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00474833,0.0001560215,0.0002811459,0.0002849533,0.002580963,0.0002966366,0.0007705917,0.0001611239,0.0002884057],"category_scores_gemma":[0.001036454,0.0001676195,0.0002929719,0.001648685,0.002617145,0.0007114568,0.00003422979,0.0005070737,0.000008462254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001505075,"about_ca_system_score_gemma":0.004898103,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1152167,"about_ca_topic_score_gemma":0.04824518,"domain_scores_codex":[0.99703,0.0001150567,0.0004568907,0.0003488076,0.001086733,0.0009625729],"domain_scores_gemma":[0.9975647,0.0001368492,0.0003655381,0.0002700914,0.0005507225,0.001112094],"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":[0.00001075764,0.00003089359,0.007992699,0.0000045227,0.00004849412,0.0000210655,0.06983808,0.0005435604,0.0503297,0.002958544,0.006049981,0.8621717],"study_design_scores_gemma":[0.003090818,0.00063826,0.2567861,0.001473844,0.0002850321,0.0004819899,0.1677702,0.03732933,0.2893947,0.1380278,0.1011316,0.003590327],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9863201,0.0001293171,0.002552045,0.002989924,0.001434234,0.0001207809,0.000005848413,0.00003193801,0.006415819],"genre_scores_gemma":[0.7554046,0.00004744823,0.2425428,0.001230933,0.0006879519,3.211913e-7,3.832203e-7,0.000008616767,0.00007701142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8585814,"threshold_uncertainty_score":0.9987175,"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."}}