{"id":"W1516927529","doi":"","title":"Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining","year":2002,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Alberta","funders":"","keywords":"LEAPS; Computer science; Data science; Knowledge extraction; Big data; Library science; World Wide Web; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009396731,0.002467464,0.005541907,0.007395422,0.002219122,0.01440859,0.00542909,0.00385903,0.04158947],"category_scores_gemma":[0.02197819,0.00159954,0.00275001,0.005755857,0.002234736,0.01123242,0.005880591,0.007281394,0.02952452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002193488,"about_ca_system_score_gemma":0.007750295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007642261,"about_ca_topic_score_gemma":0.01293541,"domain_scores_codex":[0.9889147,0.002531154,0.001280183,0.001399413,0.005309051,0.0005655119],"domain_scores_gemma":[0.984561,0.005163674,0.0008116939,0.002610703,0.004212796,0.002640135],"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":[0.0002073454,0.0002532863,0.001674706,0.0008509424,0.0002883153,0.000216331,0.0001816811,0.0009606438,0.0007063531,0.003271362,0.8051631,0.1862258],"study_design_scores_gemma":[0.00007150682,0.0001088956,0.002359412,0.0005787908,0.0001481363,0.0005622441,0.0003185787,0.005462686,0.001024385,0.0120356,0.9772399,0.00008975535],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.02400151,0.2504074,0.273394,0.08418304,0.1723962,0.00323242,0.05307577,0.02835288,0.1109568],"genre_scores_gemma":[0.05442816,0.1866696,0.268931,0.01637803,0.02456995,0.001823511,0.1952998,0.004876456,0.2470236],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.04158947,"threshold_uncertainty_score":0.1391307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1098523934291357,"score_gpt":0.312280253627247,"score_spread":0.2024278601981113,"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."}}