{"id":"W2129777864","doi":"10.1109/tgrs.2005.848706","title":"An adaptive fuzzy evidential nearest neighbor formulation for classifying remote sensing images","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"k-nearest neighbors algorithm; Fuzzy logic; Pattern recognition (psychology); Computer science; Artificial intelligence; Entropy (arrow of time); Nearest-neighbor chain algorithm; Data mining; Fuzzy set; Mathematics; Fuzzy clustering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003819238,0.0003317491,0.0002716125,0.0003678194,0.0008566701,0.0003513008,0.0001112188,0.0001879113,0.000001984854],"category_scores_gemma":[0.00002827558,0.0003457429,0.00012025,0.000452429,0.000158645,0.001202477,0.000002064517,0.0003435071,0.00001410844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002378704,"about_ca_system_score_gemma":0.00005492914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00019445,"about_ca_topic_score_gemma":0.0002461523,"domain_scores_codex":[0.9980083,0.00006625335,0.0004320248,0.0005986467,0.0003397547,0.0005550421],"domain_scores_gemma":[0.9989138,0.0001856978,0.0001046139,0.0004286992,0.0001998543,0.0001673342],"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":[0.00004217346,0.000007490622,8.169076e-8,0.00002392956,0.00001169338,0.00000369034,0.0003061505,0.03554611,0.1627455,0.000003018582,0.00001384565,0.8012964],"study_design_scores_gemma":[0.0003825463,0.0001086476,0.0001848695,0.0002005776,0.00006015704,0.0001063419,0.0002173966,0.9057084,0.09206559,0.0002024952,0.0003969285,0.0003660661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07157409,0.00006441981,0.9259751,0.0003323394,0.0008696211,0.0004307228,0.00001300796,0.000451413,0.000289304],"genre_scores_gemma":[0.6586607,0.00007271712,0.3408513,0.00007369024,0.0001900354,4.707269e-8,0.000004090155,0.00005031924,0.00009709259],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8701623,"threshold_uncertainty_score":0.9998994,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02867470428937668,"score_gpt":0.2719293869634695,"score_spread":0.2432546826740928,"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."}}