{"id":"W2617645388","doi":"10.1080/15481603.2017.1331510","title":"Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration","year":2017,"lang":"en","type":"article","venue":"GIScience & Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Centre For Cold Ocean Resources Engineering","funders":"Environment and Climate Change Canada; Natural Sciences and Engineering Research Council of Canada; Department of Environment and Conservation, Government of Newfoundland and Labrador; Government of Canada","keywords":"Wetland; Remote sensing; Confusion; Synthetic aperture radar; Geography; Aerial imagery; Environmental science; Satellite imagery; Environmental resource management; Cartography; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005042758,0.0003811486,0.0002250985,0.001942321,0.0004852932,0.0008535169,0.0003534998,0.0002009735,0.0005142309],"category_scores_gemma":[0.0005802222,0.0001869508,0.0002883061,0.001493895,0.0003813621,0.0003295647,0.0004270471,0.0001674107,0.0001843947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003015917,"about_ca_system_score_gemma":0.001876705,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8027333,"about_ca_topic_score_gemma":0.935032,"domain_scores_codex":[0.9997129,0.00003626612,0.00001832957,0.00006608832,0.00005941432,0.0001068869],"domain_scores_gemma":[0.9994819,0.0000760766,0.00009899074,0.00003773873,0.0002454347,0.00005992131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006411443,0.0002307947,0.7817986,0.0001912904,0.0002821372,0.001132438,0.0008805976,0.01181237,0.04289383,0.0002155145,0.003628402,0.1562928],"study_design_scores_gemma":[0.0000162347,0.00003457538,0.9823701,0.00001981577,0.00006605971,0.00009006492,0.0007566185,0.01215797,0.00319596,0.00001257309,0.001264394,0.00001551315],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961113,0.0002105129,0.001236178,0.00003934516,0.000005598022,0.00003078659,0.0009447131,0.000084288,0.001337286],"genre_scores_gemma":[0.9898401,0.0001957953,0.005932049,0.00003192914,0.00000433371,0.00002244604,0.002639335,0.00001357018,0.001320372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1972667,"threshold_uncertainty_score":0.3968568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07366591244811731,"score_gpt":0.3180304802489899,"score_spread":0.2443645678008726,"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."}}