{"id":"W2743281947","doi":"10.3390/rs9080807","title":"Automated Quantification of Surface Water Inundation in Wetlands Using Optical Satellite Imagery","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"South Florida Water Management District; U.S. Geological Survey; National Aeronautics and Space Administration","keywords":"Wetland; Remote sensing; Environmental science; Surface water; Satellite imagery; Vegetation (pathology); Satellite; Hydrology (agriculture); Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003224125,0.0005452447,0.0003030872,0.001415334,0.0002597556,0.0005588756,0.000550955,0.0002498778,0.0005030269],"category_scores_gemma":[0.0007752901,0.0002322413,0.0002913528,0.000664884,0.0002064372,0.0007321105,0.0004835945,0.0001932677,0.0001907888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003649321,"about_ca_system_score_gemma":0.0006428295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01193274,"about_ca_topic_score_gemma":0.02477964,"domain_scores_codex":[0.9998257,0.00002213467,0.00001013329,0.00005501198,0.00006409785,0.00002298947],"domain_scores_gemma":[0.9996552,0.00008864725,0.00007955015,0.00004636218,0.0001127885,0.00001739239],"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":[0.0001143065,0.0002877001,0.06957459,0.0001137517,0.0001567545,0.0001265689,0.0002138169,0.2133249,0.06546161,0.0008075004,0.003357609,0.6464608],"study_design_scores_gemma":[0.00001763711,0.0000386676,0.03930336,0.000008318962,0.00001741827,0.00005006781,0.00005567492,0.9462157,0.01252299,0.0008367497,0.0009079195,0.00002554985],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5520427,0.000164593,0.4405813,0.00008738387,0.0000271162,0.0001495827,0.001056983,0.004212639,0.001677643],"genre_scores_gemma":[0.7240651,0.00007320126,0.2738925,0.00003406582,0.00001847998,0.00009373628,0.001000026,0.0001103054,0.0007125743],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01193274,"threshold_uncertainty_score":0.02372652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02749997252013412,"score_gpt":0.2950328474631764,"score_spread":0.2675328749430423,"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."}}