{"id":"W2222888818","doi":"10.1007/11574798","title":"Transactions on Rough Sets IV","year":2005,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Rough set; Computer science; Business; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00215288,0.001076742,0.003372239,0.001841018,0.0008182481,0.004430317,0.0008318,0.001037404,0.01589308],"category_scores_gemma":[0.006071229,0.0006926061,0.001759374,0.003123465,0.001756877,0.002506356,0.001545179,0.002564163,0.00548701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00128171,"about_ca_system_score_gemma":0.001088005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001129929,"about_ca_topic_score_gemma":0.0007223227,"domain_scores_codex":[0.9977404,0.000611615,0.0002580739,0.0002102064,0.001103553,0.00007617834],"domain_scores_gemma":[0.9982675,0.0006294631,0.00009071825,0.0006139268,0.0003500168,0.00004836162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005436765,0.00002750241,0.0002112637,0.0004721352,0.0001521317,0.00009276816,0.0002041056,0.007456943,0.0006465279,0.6925272,0.05027179,0.2478833],"study_design_scores_gemma":[0.00002114309,0.00004758357,0.0004723606,0.0002178856,0.0001024024,0.0001836485,0.00009686628,0.01320743,0.0005016377,0.7540532,0.2310715,0.00002435096],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.007554626,0.1200667,0.6308745,0.009414542,0.01928931,0.0001814012,0.001240028,0.0007681039,0.2106108],"genre_scores_gemma":[0.2911589,0.1120892,0.353114,0.001833566,0.01087604,0.0006272031,0.002333223,0.000561156,0.2274066],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01589308,"threshold_uncertainty_score":0.05316764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01709651034703637,"score_gpt":0.2481352235547547,"score_spread":0.2310387132077183,"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."}}