{"id":"W2078180904","doi":"10.1016/j.rse.2007.02.014","title":"Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification","year":2007,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":232,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Parks Canada; Clayoquot Biosphere Trust; Ministry of Forests, Lands and Natural Resource Operations; University of Queensland","keywords":"Remote sensing; Riparian zone; Vegetation (pathology); Image resolution; Spatial analysis; Pixel; Contextual image classification; Environmental science; Satellite imagery; Image texture; Vegetation classification; Image segmentation; Geology; Segmentation; Computer science; Artificial intelligence; Ecology; Image (mathematics)","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":[0.0004067336,0.0002470686,0.0001905516,0.00149154,0.0002146261,0.0004434382,0.0002299774,0.0002442395,0.001549785],"category_scores_gemma":[0.0007986766,0.0001650447,0.0002748519,0.001070333,0.0001096908,0.0004400826,0.0002386022,0.0001828066,0.0003234831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002168209,"about_ca_system_score_gemma":0.0003523279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009497624,"about_ca_topic_score_gemma":0.01611234,"domain_scores_codex":[0.9998734,0.00002772155,0.000007612421,0.00002417649,0.0000491586,0.00001794973],"domain_scores_gemma":[0.9997341,0.0000802357,0.00002255587,0.00003907334,0.0001002094,0.00002381343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002714224,0.0003107384,0.02733546,0.0001754577,0.0001373542,0.0002144691,0.0001334225,0.03348971,0.08109006,0.00101451,0.005366638,0.8504608],"study_design_scores_gemma":[0.00010748,0.0001944337,0.147214,0.0000383011,0.0002019224,0.0003794431,0.0003483813,0.8030863,0.03415167,0.002198354,0.01202239,0.00005735568],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7023584,0.001576168,0.2702121,0.000591606,0.0001781357,0.00034853,0.003472452,0.002425871,0.01883667],"genre_scores_gemma":[0.7576129,0.000613868,0.2358793,0.0001094044,0.00004678506,0.00005834741,0.002309573,0.00008254224,0.003287394],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009497624,"threshold_uncertainty_score":0.01888466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009260019439390978,"score_gpt":0.2081799651654975,"score_spread":0.1989199457261065,"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."}}