{"id":"W4411494759","doi":"10.1007/s12190-025-02520-1","title":"Novel neutrosophic fuzzy max-min-based similarities and their application in clustering for educational decision-making support","year":2025,"lang":"en","type":"article","venue":"Journal of Applied Mathematics and Computing","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematics; Cluster analysis; Fuzzy logic; Theory of computation; Fuzzy clustering; Group decision-making; Artificial intelligence; Data mining; Computer science; Algorithm; Statistics","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":[0.004032824,0.0001883308,0.000541108,0.0007328106,0.0002232678,0.0004655544,0.0004390659,0.00008745542,0.000008080794],"category_scores_gemma":[0.001041929,0.0001428997,0.0000942434,0.0004087277,0.0000674922,0.0001185258,0.000238669,0.0001957688,8.868931e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000519126,"about_ca_system_score_gemma":0.0001664263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.367168e-7,"about_ca_topic_score_gemma":0.000008532667,"domain_scores_codex":[0.9974146,0.00001480846,0.001550168,0.0003110336,0.0004940936,0.0002152916],"domain_scores_gemma":[0.9896773,0.008735235,0.0009023139,0.0002611205,0.0003590065,0.00006509347],"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":[0.0004891178,0.000531383,0.001012688,0.0004643112,0.00005830651,0.000003439931,0.004833804,0.03155911,0.01935014,0.1059413,0.0004332266,0.8353232],"study_design_scores_gemma":[0.00093023,0.00002934916,0.002036061,0.0004888541,0.00001304256,0.00003613399,0.001995062,0.5690823,0.0001633954,0.4248083,0.0002898274,0.0001274408],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.282734,0.00008447112,0.7156817,0.0004707723,0.0001676843,0.0002588686,0.000005041953,0.00000549699,0.00059192],"genre_scores_gemma":[0.6435508,0.00000301927,0.3560856,0.0002597544,0.00007867794,0.000006532609,5.220484e-7,0.000009622661,0.000005479363],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8351958,"threshold_uncertainty_score":0.5827283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05916729549728555,"score_gpt":0.3808636969093272,"score_spread":0.3216964014120416,"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."}}