{"id":"W4412058981","doi":"10.1007/s13042-025-02729-3","title":"Accelerated optimal scale selection in dynamic multi-scale set-valued decision tables","year":2025,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Computational intelligence; Scale (ratio); Selection (genetic algorithm); Computer science; Set (abstract data type); Artificial intelligence; Mathematical optimization; Machine learning; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004581088,0.000112051,0.0001719933,0.0003472767,0.00007183615,0.0002438037,0.0004784483,0.00007035014,0.00001598429],"category_scores_gemma":[0.0001153312,0.00009427989,0.0000522939,0.0002782949,0.00002937146,0.0002269118,0.0001699755,0.000458498,0.000002685894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008442601,"about_ca_system_score_gemma":0.00006414221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009930302,"about_ca_topic_score_gemma":0.0001315042,"domain_scores_codex":[0.9988673,0.00009975497,0.0004008268,0.0001749189,0.0003152725,0.0001418834],"domain_scores_gemma":[0.9993164,0.0001132236,0.0002048537,0.00005277544,0.0002625978,0.00005016267],"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":[0.000318879,0.0004563875,0.2503925,0.00001606884,0.0001524993,0.0001093175,0.001668277,0.1809686,0.003600666,0.001181151,0.0003410981,0.5607946],"study_design_scores_gemma":[0.001249868,0.0001274268,0.06394857,0.0001240079,0.00000838187,0.00009740566,0.00005802379,0.9311917,0.0001666049,0.0009308932,0.002001487,0.00009566528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5535478,0.0007277852,0.4441348,0.0007316718,0.0004363871,0.00004159465,0.000001846141,0.00002070135,0.0003573585],"genre_scores_gemma":[0.876165,0.0004406449,0.1228198,0.0001220149,0.00003651336,8.613727e-7,0.000003513879,0.000005474088,0.0004062118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7502231,"threshold_uncertainty_score":0.3844622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01135813973383022,"score_gpt":0.302579969435275,"score_spread":0.2912218297014448,"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."}}