{"id":"W2768827643","doi":"10.1016/j.envsoft.2017.11.001","title":"Improving the catchment scale wetland modeling using remotely sensed data","year":2017,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Natural Resources Conservation Service; U.S. Fish and Wildlife Service; National Aeronautics and Space Administration","keywords":"Scale (ratio); Wetland; Environmental science; Remote sensing; Hydrology (agriculture); Drainage basin; Environmental resource management; Geography; Water resource management; Physical geography; Cartography; Geology; Ecology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004592867,0.0002942429,0.000220325,0.00002005235,0.002944955,0.000140131,0.001405063,0.00009458339,0.0001598556],"category_scores_gemma":[0.00002086263,0.0002264472,0.0000687957,0.00002590776,0.0005444079,0.0006616814,0.003367824,0.0002748139,0.0002437569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001871966,"about_ca_system_score_gemma":0.000004852392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001905964,"about_ca_topic_score_gemma":0.00008928668,"domain_scores_codex":[0.9978964,0.00006072488,0.0002889557,0.0008168038,0.0004049744,0.0005321765],"domain_scores_gemma":[0.997592,0.0000377678,0.000200248,0.00207502,0.000001463142,0.00009349159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002816153,0.00006011903,0.04679666,0.000009064171,0.00003819289,0.0000172529,0.0006167322,0.9465171,0.001635435,0.000001414187,0.0001209054,0.004158997],"study_design_scores_gemma":[0.0003357734,0.00002014813,0.001592098,0.00001765769,0.00009775508,0.000009031051,0.0001824185,0.9961075,0.0002891033,0.0004914578,0.0005570417,0.0002999868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6711718,0.00009297286,0.3276416,0.0003344996,0.0001789672,0.000280094,0.00002951692,0.00005529604,0.0002152294],"genre_scores_gemma":[0.9773929,0.0001501691,0.02162973,0.0002637904,0.00009866434,0.00000801297,0.00004888354,0.00003832163,0.0003696048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.306221,"threshold_uncertainty_score":0.9983531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05169146679663554,"score_gpt":0.2464982360795406,"score_spread":0.194806769282905,"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."}}