{"id":"W2169166714","doi":"10.1177/0956797612441217","title":"Identifying the Bad Guy in a Lineup Using Confidence Judgments Under Deadline Pressure","year":2012,"lang":"en","type":"article","venue":"Psychological Science","topic":"Deception detection and forensic psychology","field":"Psychology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Australian Research Council","keywords":"Eyewitness identification; Culprit; Witness; Psychology; Identification (biology); Confidence interval; Social psychology; Cognitive psychology; Statistics; Computer science; Data mining; Law; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002400726,0.0001999009,0.0002152044,0.0002244881,0.0003394104,0.00009143155,0.000907726,0.0001915796,0.004266467],"category_scores_gemma":[0.0001808399,0.0001309038,0.00007866119,0.001579325,0.00148344,0.0003739405,0.0001519891,0.0005333926,0.0009905845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005741203,"about_ca_system_score_gemma":0.00001541448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008352036,"about_ca_topic_score_gemma":0.00001812667,"domain_scores_codex":[0.9970763,0.00028415,0.0004496902,0.0007148887,0.0004873133,0.0009876953],"domain_scores_gemma":[0.9987019,0.0001359165,0.0001432673,0.0006892068,0.00007766407,0.0002520753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00120506,0.007488913,0.3411313,0.00003011242,0.000197544,0.0001254346,0.01724032,0.001230799,0.1568667,0.3315212,0.01782102,0.1251416],"study_design_scores_gemma":[0.0008903639,0.0001151794,0.9831217,0.00002091839,0.00001929472,0.0003613868,0.0009855961,0.0003942172,0.0001222351,0.004768318,0.008919484,0.0002812891],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9487097,0.000734749,0.01778616,0.001008411,0.006584289,0.0003730115,0.000003697422,0.0001051542,0.0246948],"genre_scores_gemma":[0.9936221,0.00001822385,0.0008481629,0.003943762,0.0002823271,0.00004052958,6.517733e-7,0.00001079797,0.001233499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6419904,"threshold_uncertainty_score":0.9997873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1718139881830653,"score_gpt":0.4701082733123452,"score_spread":0.2982942851292799,"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."}}