{"id":"W3021262956","doi":"","title":"Is there anybody out there? Detecting operational outages from LVTS transaction data","year":2020,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"False positive paradox; Payment; Database transaction; Operational risk; Interval (graph theory); Computer science; Order (exchange); Transaction data; False positives and false negatives; Test (biology); Value (mathematics); Reliability engineering; Operations research; Data mining; Engineering; Database; Business; Mathematics; Finance; Risk management; Artificial intelligence; Machine learning","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":[0.002076342,0.0004255435,0.000356335,0.002258027,0.0006939555,0.001703907,0.0009382715,0.0007195591,0.001352838],"category_scores_gemma":[0.01139254,0.0001345398,0.0001909235,0.002393086,0.0003654872,0.0009954063,0.0007048962,0.0006138503,0.0006052018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002110385,"about_ca_system_score_gemma":0.003241204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1769972,"about_ca_topic_score_gemma":0.2319099,"domain_scores_codex":[0.9982882,0.0002101602,0.000152477,0.0003071175,0.0008220752,0.0002199488],"domain_scores_gemma":[0.9944007,0.002100633,0.0009194232,0.0005532205,0.001698521,0.0003276251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008724442,0.000265658,0.6359841,0.0003042606,0.0001575254,0.0009309759,0.001393738,0.03968657,0.01566268,0.004472801,0.02909281,0.2711765],"study_design_scores_gemma":[0.00008284357,0.0001659969,0.2589096,0.00008755191,0.00007330146,0.0007739793,0.003260816,0.6871139,0.01627892,0.006652445,0.02650295,0.00009762707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.903118,0.000712099,0.06991194,0.001490617,0.0001151017,0.0004216634,0.01216709,0.001966922,0.0100966],"genre_scores_gemma":[0.9645482,0.0001299009,0.02631597,0.0000638843,0.00002805049,0.00003734636,0.00700805,0.00002866726,0.001839909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1769972,"threshold_uncertainty_score":0.3519338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2804109270350669,"score_gpt":0.4571019745580668,"score_spread":0.1766910475229999,"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."}}