{"id":"W4390050977","doi":"10.1080/07038992.2023.2293058","title":"Comprehensive Landsat-Based Analysis of Long-Term Surface Water Dynamics over Wetlands and Waterbodies in North America","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"U.S. Geological Survey; Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland","keywords":"Wetland; Surface water; Environmental science; Geography; Earth observation; Ecosystem services; Biodiversity; Ecosystem; Habitat; Hydrology (agriculture); Remote sensing; Physical geography; Ecology; Geology; Satellite","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001238151,0.0001068232,0.000289617,0.0004224227,0.00006389011,0.00003245517,0.00008594452,0.00002940291,0.0000484773],"category_scores_gemma":[0.000004943854,0.00008104562,0.00007800657,0.0006352564,0.0001287524,0.0000852248,0.0000377648,0.0001045794,0.000005656255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001742506,"about_ca_system_score_gemma":0.00002801077,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02467,"about_ca_topic_score_gemma":0.5957111,"domain_scores_codex":[0.9991109,0.00004460402,0.0002640613,0.0001268215,0.0001712973,0.000282365],"domain_scores_gemma":[0.9995517,0.00003030704,0.000110027,0.000115877,0.00002147154,0.0001706387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001898804,0.000003485761,0.8787585,0.00001902755,0.0001666119,0.0006180692,0.0008814245,0.07196169,0.0002971952,2.577434e-7,0.00009525497,0.04717948],"study_design_scores_gemma":[0.0002904522,0.00004569833,0.7547459,0.00002639719,0.0001534117,0.000005017728,0.0002417084,0.2436692,0.0001255218,0.000006938988,0.0005920149,0.00009773198],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981188,0.0000230063,0.001197609,0.0003673175,0.00008271173,0.00005481077,0.000008405053,0.000004238881,0.0001431088],"genre_scores_gemma":[0.9971761,0.00006505097,0.002543779,0.0001038816,0.00000991848,7.444797e-9,0.00003958558,0.000008908084,0.00005281071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.571041,"threshold_uncertainty_score":0.9818248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00983087526495734,"score_gpt":0.2252239643692805,"score_spread":0.2153930891043232,"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."}}