{"id":"W2007770981","doi":"10.1002/hyp.6378","title":"Modified fuzzy c‐means classification technique for mapping vague wetlands using Landsat ETM+ imagery","year":2006,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Resources Canada; University of Calgary","keywords":"Thematic Mapper; Classifier (UML); Fuzzy logic; Computer science; Artificial intelligence; Thematic map; Remote sensing; Pattern recognition (psychology); Fuzzy classification; Data mining; Satellite imagery; Fuzzy set; Cartography; Geography","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":[0.001068299,0.0004645869,0.0004444566,0.001601037,0.0004032793,0.0005501788,0.0007559058,0.0006136921,0.0008905279],"category_scores_gemma":[0.003098134,0.0001760789,0.0004540038,0.001016805,0.0003558997,0.0004230032,0.0002573747,0.0004451414,0.0002616489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007481156,"about_ca_system_score_gemma":0.0006771297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01232021,"about_ca_topic_score_gemma":0.01141063,"domain_scores_codex":[0.9995215,0.00007149144,0.00003947873,0.00008684157,0.0002531961,0.00002753884],"domain_scores_gemma":[0.9990426,0.0002899965,0.0001168239,0.00008077007,0.0004461656,0.00002373456],"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":[0.0003536097,0.0001042472,0.002819532,0.0001662671,0.0001361165,0.0001470868,0.0001833778,0.1613592,0.05341632,0.003465445,0.003595806,0.7742531],"study_design_scores_gemma":[0.00001269062,0.00002902729,0.002431698,0.000008613722,0.00002116624,0.00005956072,0.00001920938,0.9821028,0.01312539,0.001024009,0.001142101,0.00002385085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06667973,0.0001946448,0.9312696,0.0000905376,0.00006826124,0.00005793975,0.00008666629,0.0007827221,0.0007699194],"genre_scores_gemma":[0.328173,0.0001094575,0.6702912,0.00004170085,0.0000354411,0.00009519719,0.0001583544,0.00004156225,0.001053976],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01232021,"threshold_uncertainty_score":0.02449703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03622287401717351,"score_gpt":0.2467390283894506,"score_spread":0.2105161543722771,"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."}}