{"id":"W2055683524","doi":"10.1177/002204260503500404","title":"Risks of Arrest across Drug Markets: A Capture-Recapture Analysis of “Hidden” Dealer and User Populations","year":2005,"lang":"en","type":"article","venue":"Journal of Drug Issues","topic":"Census and Population Estimation","field":"Mathematics","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Odds; Vulnerability (computing); Heroin; Business; Population; Distribution (mathematics); Mark and recapture; Environmental health; Demography; Computer security; Medicine; Drug; Logistic regression; Psychiatry; Computer science; Sociology; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.005139884,0.0003446144,0.0005614041,0.001277217,0.001054156,0.0008976087,0.001844024,0.0007752044,0.001158813],"category_scores_gemma":[0.009899638,0.0004882749,0.001122006,0.0009048704,0.001063468,0.0007722791,0.001086914,0.0009435994,0.0001668799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002887079,"about_ca_system_score_gemma":0.001562668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4792986,"about_ca_topic_score_gemma":0.3940813,"domain_scores_codex":[0.9985563,0.0005275615,0.00007908274,0.0003569851,0.0001943912,0.0002857194],"domain_scores_gemma":[0.9935678,0.002452265,0.001790716,0.001300869,0.0005592361,0.0003290926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002117661,0.0001007031,0.9691377,0.00003031973,0.0006763345,0.0003305845,0.001164337,0.01546664,0.0007856938,0.002373853,0.0005831618,0.009138901],"study_design_scores_gemma":[0.00002607477,0.0001893117,0.8757964,0.00002171315,0.0004043501,0.0003585378,0.000951627,0.1203804,0.0004506809,0.0006427834,0.0007258265,0.00005240687],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948741,0.0001220565,0.00397185,0.0001051469,0.000003669206,0.00005463471,0.0005257026,0.00001531783,0.0003274708],"genre_scores_gemma":[0.9970523,0.00009454189,0.001460886,0.0000275195,0.000003838657,0.00002609737,0.0008522564,0.000004580726,0.0004779925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4792986,"threshold_uncertainty_score":0.9530176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06865879355092161,"score_gpt":0.4011834837182973,"score_spread":0.3325246901673757,"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."}}