{"id":"W7036771119","doi":"","title":"Classification study of rough sets generalization / by Han Yin Shi. --","year":2017,"lang":"en","type":"dissertation","venue":"Knowledge Commons (Lakehead University)","topic":"Digital Media and Philosophy","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Heritage; Library and Archives Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Lakehead University","keywords":"Rough set; Generalization; Feature (linguistics); Pattern recognition (psychology); Set (abstract data type)","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.0001233696,0.0003547914,0.0004789006,0.0006432045,0.0004100354,0.0002140932,0.002495858,0.0003092288,0.000008453345],"category_scores_gemma":[0.00005802474,0.0004165193,0.0001385396,0.0007367505,0.00006663577,0.00101841,0.0001797803,0.0002907237,0.00005548372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001505119,"about_ca_system_score_gemma":0.0002565836,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007909976,"about_ca_topic_score_gemma":0.03399517,"domain_scores_codex":[0.9981787,0.0002198173,0.0003359447,0.0006598848,0.0003426804,0.0002630081],"domain_scores_gemma":[0.9973652,0.00009535813,0.0005816193,0.001433458,0.0003582886,0.0001660323],"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":[0.0005114225,0.01780479,0.06297574,0.001217178,0.001189152,0.0002504468,0.07566682,0.00006046575,0.003749142,0.4722354,0.0883162,0.2760232],"study_design_scores_gemma":[0.006913787,0.002740733,0.1447205,0.001260509,0.0007879094,0.00000943719,0.002890981,0.005133908,0.003487312,0.008684672,0.8198646,0.003505689],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5059745,0.0004188978,0.002110473,0.0001624361,0.002119885,0.0008329627,0.00007304764,0.0002029126,0.4881049],"genre_scores_gemma":[0.9655793,0.000004763971,0.00003194728,0.000001380543,0.00006495059,0.000005545638,0.001024603,0.00002759733,0.03325989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7315484,"threshold_uncertainty_score":0.9998286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04876259098844647,"score_gpt":0.2786765843736565,"score_spread":0.22991399338521,"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."}}