{"id":"W4407498245","doi":"10.1007/s11634-025-00625-w","title":"Random models for adjusting fuzzy rand index extensions","year":2025,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Index (typography); Fuzzy logic; Computer science; Econometrics; Mathematics; Statistics; Artificial intelligence; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02709519,0.001515757,0.002426815,0.002986227,0.0009398346,0.003328631,0.005067119,0.002484437,0.006751842],"category_scores_gemma":[0.08941673,0.00127564,0.002669191,0.002768908,0.002104772,0.006165864,0.002755288,0.00423486,0.001555508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002092316,"about_ca_system_score_gemma":0.001295819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002950877,"about_ca_topic_score_gemma":0.003523794,"domain_scores_codex":[0.9868886,0.008756097,0.0003474732,0.002172385,0.001399335,0.0004359932],"domain_scores_gemma":[0.9557358,0.03121128,0.002604575,0.007689727,0.002289296,0.0004692587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003364939,0.0001472688,0.002042898,0.0001643275,0.0003680981,0.0001045462,0.0002288476,0.5566649,0.0005851823,0.3380443,0.004451964,0.0968613],"study_design_scores_gemma":[0.00002639652,0.00004741302,0.0003916636,0.00003245072,0.00004571984,0.00003590464,0.00001725321,0.8743458,0.0002173339,0.1231766,0.001630403,0.00003285845],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007361723,0.0003448383,0.9902499,0.0002008078,0.00008122703,0.00006050643,0.00008259199,0.0002851646,0.001333176],"genre_scores_gemma":[0.4249198,0.0008763602,0.5561008,0.0005082238,0.0003676965,0.0007363635,0.0009056834,0.0007098223,0.0148753],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02709519,"threshold_uncertainty_score":0.1432948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05220024079240108,"score_gpt":0.3365206615945156,"score_spread":0.2843204208021145,"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."}}