{"id":"W4415451460","doi":"10.1016/j.asoc.2025.114114","title":"Fuzzy natural neighbors for outlier detection","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Fondo de Fomento al Desarrollo Científico y Tecnológico; Agencia Nacional de Investigación y Desarrollo; Instituto de Sistemas Complejos de Ingeniería","keywords":"Outlier; Fuzzy logic; Anomaly detection; Ambiguity; Benchmarking; Flexibility (engineering)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001506519,0.0003889394,0.00114791,0.001961665,0.0007298267,0.001060535,0.001078632,0.000895413,0.001085425],"category_scores_gemma":[0.004763199,0.0002552674,0.0006375682,0.001442994,0.0008205461,0.001505891,0.0006678441,0.0009650182,0.000229522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007503444,"about_ca_system_score_gemma":0.0007435236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004839272,"about_ca_topic_score_gemma":0.005010018,"domain_scores_codex":[0.9987117,0.0002749034,0.0000781515,0.0002959203,0.000566732,0.00007272221],"domain_scores_gemma":[0.9979553,0.001036043,0.0001727368,0.0001960628,0.0005703608,0.00006949371],"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":[0.0006057883,0.0002456849,0.004016851,0.0003142452,0.0001709058,0.0002796438,0.0002488072,0.3196841,0.01688799,0.09779377,0.004818378,0.5549338],"study_design_scores_gemma":[0.000005179316,0.0000341135,0.0003834638,0.000006746809,0.00001116183,0.00004892507,0.00002762103,0.9810286,0.001959875,0.01561214,0.000871908,0.00001023672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02393882,0.0005985814,0.9739189,0.00009417316,0.00006872597,0.00003350107,0.00006639206,0.0002961783,0.0009847601],"genre_scores_gemma":[0.5822933,0.0004410115,0.4132307,0.00007063287,0.0001248208,0.0001004985,0.0002562806,0.00007492874,0.003407878],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004839272,"threshold_uncertainty_score":0.009622216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006518451004324022,"score_gpt":0.2520087350476332,"score_spread":0.2454902840433092,"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."}}