{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001358026,0.00007234482,0.00005484243,0.00001386775,0.0001663626,0.00003714151,0.00002485412,0.00002465612,0.0000431049],"category_scores_gemma":[0.000004222509,0.00004419867,0.000014252,0.00003419474,0.00003971351,0.0002145089,0.000108638,0.00002985039,0.00005053958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001338038,"about_ca_system_score_gemma":0.000001699204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005370287,"about_ca_topic_score_gemma":0.00003075236,"domain_scores_codex":[0.9994058,0.00002637878,0.00008229758,0.0001753644,0.000148795,0.0001613845],"domain_scores_gemma":[0.9998477,0.00001241203,0.00001628991,0.00008355533,0.00000408093,0.00003598888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003202301,0.000001474153,0.0009184395,0.000001390572,0.000003875664,0.000003304795,0.00006187854,0.001156779,0.8226486,8.950822e-7,0.000005983633,0.1751942],"study_design_scores_gemma":[0.0003885042,0.00002270804,0.00569047,0.00005290075,0.00002130932,0.00002822683,0.00006939776,0.2148954,0.7737142,0.0002705953,0.004631953,0.0002142996],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9203185,0.00001052261,0.07858415,0.0002273391,0.0002057907,0.00006905384,1.303611e-7,0.00002661473,0.0005579247],"genre_scores_gemma":[0.9577363,0.00002690917,0.04173305,0.00001986456,0.00007160641,1.289216e-9,2.120401e-7,0.000008667762,0.0004034151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2137387,"threshold_uncertainty_score":0.1802369,"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."}}