{"id":"W2330427943","doi":"10.3390/rs8040285","title":"Operational Surface Water Detection and Monitoring Using Radarsat 2","year":2016,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Environment and Climate Change Canada","funders":"Canadian Space Agency","keywords":"Remote sensing; Environmental science; Synthetic aperture radar; Environmental resource management; Meteorology; Geology; Geography","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":[0.0003834157,0.000355321,0.0001894557,0.0008792287,0.0001852595,0.0005830131,0.000411337,0.0001865855,0.001457833],"category_scores_gemma":[0.0005887289,0.00009286142,0.00009593458,0.0008709756,0.0001739213,0.0004662499,0.0003399174,0.000201204,0.0005238826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005511708,"about_ca_system_score_gemma":0.000873488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03762031,"about_ca_topic_score_gemma":0.07273339,"domain_scores_codex":[0.999669,0.00004048228,0.000008029941,0.00004746699,0.0001842673,0.00005082663],"domain_scores_gemma":[0.9996985,0.00003755596,0.00003758645,0.00003206786,0.0001712692,0.00002308677],"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":[0.000318537,0.0002359784,0.06362571,0.0001706815,0.00006895372,0.000146025,0.000240462,0.03451583,0.2848916,0.002082524,0.01021529,0.6034884],"study_design_scores_gemma":[0.0001498518,0.0005867572,0.3025429,0.00005751222,0.0001100916,0.0003473499,0.0005017006,0.4830437,0.1755178,0.002722404,0.03426743,0.0001525294],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6819005,0.000579868,0.2748181,0.0003538188,0.00008087062,0.0004039163,0.005326389,0.005012482,0.031524],"genre_scores_gemma":[0.8636547,0.0003617827,0.1272244,0.0001260586,0.00002698197,0.0001008577,0.004606801,0.00008019831,0.003818265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03762031,"threshold_uncertainty_score":0.07480264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01428823898299051,"score_gpt":0.2363243001907608,"score_spread":0.2220360612077703,"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."}}