{"id":"W4392287761","doi":"10.1029/2023gl105039","title":"Real‐Time Water Levels Using GNSS‐IR: A Potential Tool for Flood Monitoring","year":2024,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; Université du Québec à Montréal; McGill University; Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"GNSS applications; Reflectometry; Environmental science; Remote sensing; Water level; Satellite system; Flood myth; Global Positioning System; Satellite; Computer science; Geology; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007324847,0.0001745785,0.0001617524,0.0001057445,0.0003467543,0.0003499905,0.0003683319,0.00005053899,0.0004347014],"category_scores_gemma":[0.00002224008,0.0001306263,0.0001624823,0.0002764668,0.0002015629,0.0004566083,0.0006627514,0.000313135,0.002084801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003503517,"about_ca_system_score_gemma":0.00001631663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008544953,"about_ca_topic_score_gemma":0.000002412631,"domain_scores_codex":[0.9970304,0.0001145963,0.000189299,0.0005877219,0.001044701,0.001033235],"domain_scores_gemma":[0.9994081,0.0001332347,0.00001179621,0.0003025928,0.00001519543,0.0001291438],"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":[0.00002788211,0.00007548367,0.0001173471,0.0000519134,0.00005515232,0.00007846356,0.0002284164,0.001191423,0.980405,0.000114008,0.011976,0.005678927],"study_design_scores_gemma":[0.002943694,0.001113729,0.04592483,0.0005014646,0.0003572583,0.00001432114,0.0004322068,0.3712304,0.4801029,0.01106039,0.08388804,0.002430827],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921795,0.000007789769,0.00283427,0.00319782,0.0003982961,0.0006574907,0.00001167341,0.0001153333,0.0005978205],"genre_scores_gemma":[0.9906914,0.00001251457,0.004819915,0.00008943187,0.001037699,0.0001333835,0.0000127674,0.0000468893,0.003156001],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5003021,"threshold_uncertainty_score":0.9986922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0425793173312811,"score_gpt":0.3442487983259741,"score_spread":0.301669480994693,"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."}}