{"id":"W2991298551","doi":"10.1080/07038992.2019.1691516","title":"The Synergistic Use of RADARSAT-2 Ascending and Descending Images to Improve Surface Water Detection Accuracy in Alberta, Canada","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; University of Alberta; Alberta Biodiversity Monitoring Institute","funders":"","keywords":"Geography; Cartography; Remote sensing; Environmental science; Physical geography; Forestry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0003638176,0.00009779272,0.0001438017,0.00007595753,0.000138956,0.00009037717,0.00009907568,0.00002409353,0.0000164024],"category_scores_gemma":[0.0001465429,0.00006872997,0.00002619577,0.0001188832,0.00004430255,0.0002339749,0.00004325439,0.0001446307,0.000003282236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006817883,"about_ca_system_score_gemma":0.0001339905,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9764032,"about_ca_topic_score_gemma":0.9950563,"domain_scores_codex":[0.9990444,0.00005489852,0.0002688552,0.0001252691,0.000178319,0.0003283054],"domain_scores_gemma":[0.9993232,0.0001792334,0.0001263494,0.0001277386,0.00001782897,0.0002256228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005534531,0.000003561088,0.03127069,0.00003689337,0.00005569035,0.000252529,0.00120084,0.01595857,0.2492729,0.000009658661,0.001013897,0.7008694],"study_design_scores_gemma":[0.002035191,0.0004289982,0.2265979,0.0009668911,0.0001879624,0.0003173587,0.00382181,0.09625891,0.4632902,0.0002515024,0.2046599,0.001183329],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965916,0.00002454423,0.001783686,0.0007013622,0.0005451778,0.0001520666,9.960258e-7,9.939797e-7,0.0001996013],"genre_scores_gemma":[0.9929415,0.00001837095,0.006604787,0.00007509931,0.00001827035,5.561477e-9,2.740883e-7,0.0000108546,0.0003308613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6996861,"threshold_uncertainty_score":0.2802727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006818608142449788,"score_gpt":0.2002790319670111,"score_spread":0.1934604238245614,"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."}}