{"id":"W2596963603","doi":"10.2196/resprot.7499","title":"Biometrics and Policing: A Protocol for Multichannel Sensor Data Collection and Exploratory Analysis of Contextualized Psychophysiological Response During Law Enforcement Operations","year":2017,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"RTI International","keywords":"Law enforcement; Biometrics; Data collection; Officer; Computer security; Applied psychology; Computer science; Psychology; Law; Political science; Sociology","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":[0.02680343,0.00224252,0.00195862,0.002595591,0.004264833,0.002071376,0.002350223,0.003030555,0.04097013],"category_scores_gemma":[0.02396376,0.001947295,0.001686392,0.002109862,0.002564833,0.001738846,0.003121106,0.004827648,0.0182815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001662168,"about_ca_system_score_gemma":0.01190904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001925922,"about_ca_topic_score_gemma":0.005405231,"domain_scores_codex":[0.9832436,0.008998203,0.002715119,0.001656682,0.002413937,0.0009724331],"domain_scores_gemma":[0.9799881,0.004481169,0.001540984,0.006443482,0.006196128,0.001350167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01895834,0.01486005,0.01307052,0.01127934,0.0003409378,0.003089803,0.01647442,0.005181543,0.06763472,0.02891761,0.3711744,0.4490183],"study_design_scores_gemma":[0.008421567,0.01331886,0.04832105,0.007394477,0.0001872029,0.001247382,0.004659368,0.009204901,0.02374279,0.01854159,0.86423,0.0007308158],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.01139903,0.0003075557,0.1268735,0.001053518,0.0007283976,0.8373096,0.009380766,0.001841018,0.01110665],"genre_scores_gemma":[0.004462072,0.0001759488,0.05096635,0.0004481818,0.00005690441,0.9411373,0.0009457284,0.0001011287,0.001706479],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.04097013,"threshold_uncertainty_score":0.1417518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6517349187735936,"score_gpt":0.6451517332946015,"score_spread":0.00658318547899206,"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."}}