{"id":"W2121835841","doi":"10.1207/s15516709cog2503_2","title":"Detecting deception: adversarial problem solving in a low base‐rate world","year":2001,"lang":"en","type":"article","venue":"Cognitive Science","topic":"Deception detection and forensic psychology","field":"Psychology","cited_by":176,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Audit; Deception; Heuristics; Context (archaeology); Computer science; Financial statement; Adversarial system; Commit; Process (computing); Field (mathematics); Computer security; Accounting; Psychology; Artificial intelligence; Business; Social psychology; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01190025,0.0008431656,0.001051721,0.001298352,0.001671453,0.003915882,0.002532998,0.002674124,0.001785707],"category_scores_gemma":[0.07604398,0.0006722289,0.0007085165,0.0007417186,0.00534352,0.005792017,0.003554592,0.003137145,0.0003011803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001925604,"about_ca_system_score_gemma":0.001117604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004350308,"about_ca_topic_score_gemma":0.001643047,"domain_scores_codex":[0.9872041,0.008724125,0.0003376931,0.001708079,0.001394164,0.0006318981],"domain_scores_gemma":[0.8871018,0.08979408,0.008733259,0.01092322,0.002046109,0.001401673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001353543,0.001331347,0.06938499,0.000263078,0.000566608,0.001369909,0.008712322,0.7261349,0.01284383,0.09061053,0.002382609,0.08504643],"study_design_scores_gemma":[0.00005792896,0.0001487457,0.003126499,0.000019242,0.00002902633,0.0001745675,0.0005278526,0.9456299,0.002293134,0.04738656,0.0005717665,0.00003477006],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7686318,0.00009158297,0.224351,0.001850312,0.00002317798,0.0001603933,0.00007465495,0.0002747041,0.004542385],"genre_scores_gemma":[0.976404,0.0000213991,0.02313514,0.0001177763,0.0000123045,0.00003348874,0.00003348996,0.00001405132,0.0002283273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01190025,"threshold_uncertainty_score":0.06293529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0377463846277769,"score_gpt":0.340141137980055,"score_spread":0.3023947533522781,"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."}}