{"id":"W7094047021","doi":"","title":"Agents and data mining interaction 6th International Workshop on Agents and Data Mining Interaction, ADMI 2010, Toronto, On, Canada, May 11, 2010 : revised selected papers","year":2010,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Field (mathematics); Association rule learning; Knowledge extraction; Sequential Pattern Mining; Data stream 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":[0.006554623,0.001339181,0.001966021,0.001659683,0.002094606,0.008828321,0.002051712,0.001793312,0.01947257],"category_scores_gemma":[0.009568682,0.00110793,0.001213561,0.002150974,0.001496779,0.004338483,0.003426084,0.003802274,0.005329348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003234035,"about_ca_system_score_gemma":0.006335295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02818949,"about_ca_topic_score_gemma":0.05953977,"domain_scores_codex":[0.9970838,0.00096,0.0002634736,0.0005027779,0.0009352859,0.0002545853],"domain_scores_gemma":[0.9918312,0.002333581,0.0001829871,0.001143351,0.003611672,0.0008971409],"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":[0.0006471186,0.0003254422,0.003722667,0.0008301596,0.0003885565,0.000482965,0.001538742,0.01104413,0.00610718,0.05454246,0.477702,0.4426686],"study_design_scores_gemma":[0.0001057239,0.0001628714,0.003864561,0.0004148615,0.0002553802,0.0006103795,0.0009480965,0.06570616,0.008376391,0.0534594,0.8659815,0.0001146902],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01455329,0.03783427,0.8464701,0.02902259,0.01464561,0.0005019199,0.00202475,0.003438859,0.05150861],"genre_scores_gemma":[0.1366721,0.03378239,0.5083998,0.003344731,0.005677124,0.0005532251,0.01049581,0.001885331,0.2991894],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02818949,"threshold_uncertainty_score":0.06514221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06102546242624644,"score_gpt":0.3368174663631325,"score_spread":0.275792003936886,"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."}}