{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001841421,0.0005097094,0.0007452221,0.0009887636,0.000343194,0.0009852819,0.001745772,0.001153488,0.0008313809],"category_scores_gemma":[0.003002366,0.000255065,0.0006599579,0.0009372236,0.0007505175,0.001708506,0.0007092445,0.001068988,0.0002124945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006493305,"about_ca_system_score_gemma":0.0005518253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001499214,"about_ca_topic_score_gemma":0.002012627,"domain_scores_codex":[0.99882,0.0003256299,0.00008638906,0.0002217486,0.0005041498,0.00004202885],"domain_scores_gemma":[0.9992976,0.0003027755,0.0001000062,0.00006185094,0.0002207721,0.00001690698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006751688,0.00008806264,0.0006919526,0.000303388,0.00009443878,0.000186933,0.0001991106,0.6027712,0.008570824,0.1022627,0.00131145,0.2834524],"study_design_scores_gemma":[0.000004268661,0.00004379821,0.0001627326,0.00000996241,0.00001265054,0.0000559856,0.00001214251,0.982238,0.001104288,0.01503111,0.001311441,0.00001365156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002351481,0.0002105928,0.9967874,0.00004627399,0.00001699528,0.00002139501,0.00001290124,0.00001507091,0.0005377939],"genre_scores_gemma":[0.2270413,0.0006272551,0.7699328,0.00008772679,0.0001603304,0.0001608473,0.00008886591,0.00001890439,0.001881955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001841421,"threshold_uncertainty_score":0.009738505,"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."}}