{"id":"W2121897210","doi":"10.1109/tai.2002.1180827","title":"TimeSleuth: a tool for discovering causal and temporal rules","year":2003,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Causality (physics); Association rule learning; Bayesian network; Artificial intelligence; Data mining; Tree (set theory); Preprocessor; Machine learning; Data science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000104431,0.00005027417,0.00005252924,0.000017209,0.00008597676,0.0001587009,0.0001476209,0.00001407424,0.000006435204],"category_scores_gemma":[0.00002016352,0.00004126444,0.00001345248,0.00005626468,0.00001510082,0.0002831241,0.00005842386,0.00002050355,0.00001201794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000489481,"about_ca_system_score_gemma":0.00002191916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002347954,"about_ca_topic_score_gemma":0.000003743189,"domain_scores_codex":[0.9995846,0.000005271293,0.0000729029,0.0001819323,0.00004596491,0.0001093787],"domain_scores_gemma":[0.9996916,0.00004413588,0.00001660654,0.0002033249,0.00001218253,0.00003211596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[4.957371e-7,0.00002281977,0.0005104768,0.000005737126,0.000005818764,5.974156e-7,0.000122342,0.000002499084,0.0001981772,0.9370975,0.002802252,0.05923131],"study_design_scores_gemma":[0.001127535,0.0001802039,0.00562033,0.00002632276,0.00001540914,0.00006607857,0.0001326295,0.4003618,0.005464046,0.03745976,0.5488397,0.0007061572],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01903942,0.00002794004,0.9779118,0.0002679008,0.00004681084,0.0001022464,0.00001955768,0.00006264198,0.002521732],"genre_scores_gemma":[0.1169969,0.000003660719,0.8811806,0.0001192337,0.00002182759,0.00005017311,0.000009587172,0.000004523382,0.001613527],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8996377,"threshold_uncertainty_score":0.1682715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02023488657022956,"score_gpt":0.2633571375755482,"score_spread":0.2431222510053186,"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."}}