{"id":"W4233604128","doi":"10.1007/978-3-319-25783-9","title":"Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing","year":2015,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Granular computing; Rough set; Computer science; Data mining; Fuzzy set; Fuzzy logic; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.003085473,0.0008334491,0.0009465409,0.0006917246,0.0005022943,0.001643756,0.01004487,0.0004965929,0.000003773845],"category_scores_gemma":[0.0002309247,0.0007284978,0.00008338393,0.001442302,0.001045551,0.001425882,0.01055801,0.0009660407,0.00002592076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003523992,"about_ca_system_score_gemma":0.001824396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005787865,"about_ca_topic_score_gemma":0.00009361809,"domain_scores_codex":[0.9928389,0.00016234,0.0007726924,0.003316835,0.001630359,0.001278835],"domain_scores_gemma":[0.9939785,0.000642626,0.0004604803,0.004186984,0.0002878531,0.0004435737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004052231,0.00004156305,0.0002230765,0.00007641006,0.00001364868,0.0002426264,0.001646071,0.004776421,0.000002117379,0.000649582,0.00756968,0.9847547],"study_design_scores_gemma":[0.000469927,0.0001944093,0.0002049322,0.0003798982,0.00001740443,0.0003069267,8.424944e-7,0.919014,0.000008300721,0.06605999,0.01236543,0.0009779205],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002135841,0.003912074,0.9865506,0.0008841959,0.002592532,0.00048595,0.00004765727,0.000301799,0.005011638],"genre_scores_gemma":[0.0127632,0.0001219208,0.9834297,0.002567575,0.0007657963,0.000004022511,0.0001431282,0.00005429453,0.0001504152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9837768,"threshold_uncertainty_score":0.9995166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04374779765459733,"score_gpt":0.2837184353163927,"score_spread":0.2399706376617954,"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."}}