{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00518309,0.0003053177,0.0005297696,0.0003201163,0.000537656,0.0008852412,0.00334384,0.0004328856,0.00279399],"category_scores_gemma":[0.002012282,0.0002807229,0.0001802566,0.0002517453,0.000253466,0.0004874663,0.00151924,0.001958607,0.0004489308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002792876,"about_ca_system_score_gemma":0.0005798809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001471962,"about_ca_topic_score_gemma":0.0006264664,"domain_scores_codex":[0.9946357,0.0008415733,0.001119441,0.002089722,0.0008714006,0.0004421348],"domain_scores_gemma":[0.9933845,0.002884034,0.0003742728,0.002902704,0.0002308567,0.0002236248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002513871,0.0002392045,0.007710963,0.00003511185,0.0003172367,0.00001909384,0.004835317,0.01445251,0.002553887,0.001400158,0.00120491,0.9669802],"study_design_scores_gemma":[0.001407592,0.0001300656,0.02698695,0.0002539478,0.00006407365,0.00001267742,0.01978232,0.269535,0.006194372,0.3362685,0.3377791,0.001585443],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.853617,0.0006927648,0.01299491,0.02218633,0.002079972,0.002533159,0.00556678,0.000241325,0.1000878],"genre_scores_gemma":[0.9929152,0.001306396,0.00146069,0.000285936,0.0006599696,0.0001570026,0.0002853082,0.00004967102,0.002879804],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9653948,"threshold_uncertainty_score":0.9999645,"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."}}