{"id":"W1577547142","doi":"10.1007/11430919_86","title":"The TIMERS II Algorithm for the Discovery of Causality","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Causality (physics); Computer science; Algorithm; Artificial intelligence; Data mining; Theoretical computer science","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":[0.009555518,0.0002654787,0.0003851819,0.0002248373,0.0008758458,0.0007816733,0.004836619,0.0001182497,0.00003151305],"category_scores_gemma":[0.000951109,0.0001269588,0.000209204,0.0004252115,0.00211482,0.0005003261,0.001997638,0.0003354231,0.00002242836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009416958,"about_ca_system_score_gemma":0.0002647333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005677484,"about_ca_topic_score_gemma":0.0004543916,"domain_scores_codex":[0.9957047,0.00006964752,0.0008091686,0.0009025186,0.002107997,0.0004059847],"domain_scores_gemma":[0.989512,0.00771038,0.0005200766,0.00191014,0.0002942549,0.00005316162],"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":[0.000006741015,0.00001262272,0.0000017146,0.000003813799,0.00001515245,0.000001020928,0.0002612905,0.003545868,0.000002944691,0.01961434,0.001306734,0.9752278],"study_design_scores_gemma":[0.0001973082,0.0001431103,0.0001240064,0.00006736808,0.00002816582,0.000003881177,0.000005942735,0.206197,0.0002361233,0.3865034,0.4061948,0.0002988961],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000009548019,0.00063892,0.9892521,0.005517476,0.001790057,0.0006157188,0.0001291057,0.0000138225,0.002033234],"genre_scores_gemma":[0.09394646,0.001718861,0.7498513,0.02370234,0.006763624,0.0001955448,0.00009066045,0.0001689546,0.1235622],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9749289,"threshold_uncertainty_score":0.8987722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08055751605725435,"score_gpt":0.3605419344042727,"score_spread":0.2799844183470183,"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."}}