{"id":"W3018981771","doi":"10.3390/rs12081353","title":"A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR)","year":2020,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Environmental Resilience Institute, Indiana University; Fundação de Amparo à Pesquisa do Estado de São Paulo; National Institute of Education; European Space Agency; U.S. Department of Agriculture; National Aeronautics and Space Administration","keywords":"Digital elevation model; Synthetic aperture radar; Remote sensing; Elevation (ballistics); Radar; Satellite; Water level; Environmental science; Geology; Context (archaeology); Range (aeronautics); Computer science; Geography; Cartography","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":[],"consensus_categories":[],"category_scores_codex":[0.0002692856,0.0001518436,0.0001845653,0.00001917793,0.00007805118,0.00003234632,0.0002016136,0.00003888476,0.00004727579],"category_scores_gemma":[0.00002165808,0.00008281606,0.00003728076,0.0003666906,0.00006275115,0.00007489252,0.0003199698,0.0001158601,0.0000605697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001997934,"about_ca_system_score_gemma":0.00001947611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001236489,"about_ca_topic_score_gemma":0.001099181,"domain_scores_codex":[0.9986112,0.000107705,0.000209687,0.0003195152,0.0004425012,0.0003094578],"domain_scores_gemma":[0.9995465,0.0000296099,0.00004816087,0.0002777673,0.000008305644,0.00008966433],"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":[0.0005467009,0.000343834,0.01098199,0.0003099536,0.0003051348,0.001013325,0.01297519,0.3323259,0.3014591,0.0004339198,0.01243407,0.3268709],"study_design_scores_gemma":[0.0005773088,0.0002136753,0.02264905,0.0001261188,0.00002889507,0.00001230005,0.0007044575,0.9480594,0.003817135,0.00007106973,0.02347437,0.0002662388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9242859,0.00001346229,0.03088361,0.02829798,0.00008116872,0.0008095494,0.00001034697,0.0001531628,0.01546481],"genre_scores_gemma":[0.9446737,0.000004365837,0.05393672,0.001267327,0.00001716853,5.669959e-8,0.00000488868,0.00001339103,0.00008242708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6157335,"threshold_uncertainty_score":0.3377141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809622382017143,"score_gpt":0.251906466598299,"score_spread":0.2338102427781276,"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."}}