{"id":"W581782849","doi":"","title":"KDD-2002 : proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23-26, 2002, Edmonton, Alberta, Canada","year":2002,"lang":"en","type":"book","venue":"Association for Computing Machinery eBooks","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Knowledge extraction; Library science; Data science; Computer science; Data mining","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":[0.002347223,0.001771622,0.003795034,0.003360339,0.001253593,0.007130042,0.003231635,0.001143912,0.05764824],"category_scores_gemma":[0.004729233,0.001538677,0.0009960035,0.006005215,0.0008251228,0.003450743,0.001834089,0.00297984,0.05350459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002089072,"about_ca_system_score_gemma":0.00789278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05551121,"about_ca_topic_score_gemma":0.1447436,"domain_scores_codex":[0.9986991,0.00008661584,0.00007589416,0.0001387895,0.0009257226,0.0000738688],"domain_scores_gemma":[0.9952773,0.0008329603,0.00009364255,0.0004680266,0.002640374,0.0006877807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004779919,0.00005848363,0.0002082153,0.0002548647,0.00002745541,0.00003970719,0.00003566156,0.000705264,0.0003919714,0.001789437,0.8704868,0.1259542],"study_design_scores_gemma":[0.00004830052,0.0000425232,0.00152982,0.0001809775,0.0000679174,0.0003086148,0.0000699142,0.008900159,0.001864394,0.008235243,0.9787049,0.00004732418],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.007424499,0.1081176,0.4838264,0.02206217,0.04675171,0.001194053,0.08200086,0.05037503,0.1982477],"genre_scores_gemma":[0.01060275,0.0699491,0.2076094,0.002381473,0.002197564,0.0002642609,0.126025,0.005600837,0.5753698],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05764824,"threshold_uncertainty_score":0.1928526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02990121733667554,"score_gpt":0.2639706669744599,"score_spread":0.2340694496377844,"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."}}