{"id":"W2000299034","doi":"10.1080/01431160310001642296","title":"Contextual classification of Landsat TM images to forest inventory cover types","year":2004,"lang":"en","type":"article","venue":"International Journal of Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Smoothing; Random forest; Spatial contextual awareness; Pattern recognition (psychology); Pixel; Spatial analysis; Computer science; Classifier (UML); Statistics; Land cover; Remote sensing; Artificial intelligence; Mathematics; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008564183,0.0004670753,0.0003261903,0.0009243027,0.0003337971,0.0004951716,0.0002724786,0.0002231532,0.00118038],"category_scores_gemma":[0.003846938,0.0001257168,0.0004969562,0.0008703719,0.0002612426,0.0004946841,0.0006080504,0.0002925427,0.0004100351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004446554,"about_ca_system_score_gemma":0.0004029223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01038255,"about_ca_topic_score_gemma":0.02154343,"domain_scores_codex":[0.999532,0.0001105613,0.00002821213,0.0001418404,0.0001197835,0.00006763441],"domain_scores_gemma":[0.9984428,0.0003477419,0.0002324661,0.0002238425,0.0006907589,0.0000623299],"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":[0.001692282,0.0002813635,0.2533869,0.0004183479,0.000285584,0.0001068793,0.0004677691,0.08569348,0.06351817,0.001484264,0.003902486,0.5887624],"study_design_scores_gemma":[0.00006711375,0.0007924492,0.4722619,0.0001206561,0.0003415625,0.0002570981,0.000721816,0.480678,0.03682772,0.0029541,0.004870254,0.0001073987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9040993,0.0005602803,0.08981252,0.00007777398,0.00004460072,0.0001780668,0.001102413,0.001055881,0.003069133],"genre_scores_gemma":[0.953664,0.0001038973,0.04466034,0.00002738903,0.00002922474,0.00005400269,0.001052163,0.00003767196,0.0003712551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01038255,"threshold_uncertainty_score":0.02064425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361122644622987,"score_gpt":0.2542831986349662,"score_spread":0.2406719721887364,"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."}}