{"id":"W4411219679","doi":"10.3982/qe2585","title":"Real‐time detection of local no‐arbitrage violations","year":2025,"lang":"en","type":"article","venue":"Quantitative Economics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Arbitrage; Business; Geology; Finance","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":[0.004591884,0.0004096254,0.0009997527,0.0008298469,0.0003505281,0.001468016,0.001025642,0.0009984956,0.0006878256],"category_scores_gemma":[0.0361723,0.000319272,0.0003726112,0.000455627,0.001338835,0.001751368,0.001488668,0.001954143,0.0001732786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004343998,"about_ca_system_score_gemma":0.0007522943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003674955,"about_ca_topic_score_gemma":0.000409641,"domain_scores_codex":[0.9983383,0.0004702112,0.0001281592,0.0003913467,0.0005326652,0.0001392024],"domain_scores_gemma":[0.9708393,0.01914882,0.005762964,0.002209862,0.001253498,0.0007855226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001264668,0.0004121628,0.1493085,0.0003702579,0.0004783977,0.001733648,0.0009333309,0.4116497,0.0632765,0.1154082,0.001740144,0.2534244],"study_design_scores_gemma":[0.00001809339,0.0001943454,0.009807135,0.00002338915,0.00001962095,0.0004351091,0.00005997291,0.9503617,0.01159193,0.02691223,0.0005334445,0.00004303482],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4083409,0.0003112102,0.5890319,0.0002598726,0.00004477371,0.00004579865,0.00005865787,0.0003886183,0.00151832],"genre_scores_gemma":[0.9822544,0.00004507444,0.01731471,0.00003557805,0.00001824894,0.00001561893,0.00003665386,0.00001561363,0.0002640498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004591884,"threshold_uncertainty_score":0.02428448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06787552623512536,"score_gpt":0.4102036425789541,"score_spread":0.3423281163438288,"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."}}