{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004342149,0.0002699593,0.0002090184,0.00009842595,0.000294637,0.00070292,0.002391714,0.00009100996,0.000474777],"category_scores_gemma":[0.000498637,0.0002520297,0.00001437114,0.0002223507,0.0000465265,0.0023711,0.001694625,0.0003405335,0.00001249376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001201733,"about_ca_system_score_gemma":0.0002140054,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0546068,"about_ca_topic_score_gemma":0.3403945,"domain_scores_codex":[0.9975138,0.00005606357,0.00042238,0.001254471,0.0004404838,0.0003127456],"domain_scores_gemma":[0.99677,0.0003785586,0.0002538171,0.002206437,0.0001389026,0.0002522389],"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":[0.00002419915,0.0001162312,0.002964201,0.000009875525,0.0000888198,0.00001097836,0.0003721558,0.000006980847,0.0002920493,0.0002720733,0.6861676,0.3096749],"study_design_scores_gemma":[0.0006143271,0.00004891134,0.05305733,0.0002061728,0.00003266873,0.00007571256,0.0008580578,0.379522,0.00007518766,0.000003985243,0.565065,0.0004405853],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7836527,0.0002278249,0.06765205,0.05843053,0.03737389,0.00245879,0.004134236,0.001155071,0.04491487],"genre_scores_gemma":[0.8080521,0.0003923546,0.163791,0.005145784,0.0009271048,0.00006997928,0.007174518,0.00007894615,0.01436818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3795151,"threshold_uncertainty_score":0.9999932,"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."}}