{"id":"W2042788244","doi":"10.1007/s10712-008-9036-0","title":"Monitoring Flood and Discharge Variations in the Large Siberian Rivers From a Multi-Satellite Technique","year":2008,"lang":"en","type":"article","venue":"Surveys in Geophysics","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":98,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"ArcticNet; National Aeronautics and Space Administration","keywords":"Streamflow; Environmental science; Permafrost; Snow; Flood myth; Precipitation; Satellite; Snowmelt; Climatology; Discharge; Surface runoff; Drainage basin; Flood forecasting; Arctic; Hydrology (agriculture); Climate change; Geology; Meteorology; Oceanography; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000409037,0.0002976652,0.0002328797,0.001260691,0.0003588254,0.0003427929,0.000246577,0.0003181369,0.0004704657],"category_scores_gemma":[0.0004546686,0.0001700158,0.0002229278,0.001321959,0.0001336469,0.0004620446,0.0004056358,0.0001660465,0.0001072415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002891338,"about_ca_system_score_gemma":0.0003964325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00954362,"about_ca_topic_score_gemma":0.02157264,"domain_scores_codex":[0.9998593,0.00002444066,0.00001271304,0.00005009459,0.00003293072,0.0000205053],"domain_scores_gemma":[0.999705,0.00004963053,0.00007646852,0.00004221108,0.00007826221,0.00004835345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003443011,0.0001547884,0.8056121,0.00008085058,0.0002753093,0.0002070551,0.0005554878,0.009583046,0.08695473,0.0002173896,0.0008165514,0.09519842],"study_design_scores_gemma":[0.00001364303,0.00006215106,0.9783455,0.000007230102,0.00007724651,0.0001055681,0.0001082172,0.01750333,0.003081827,0.00006203399,0.00062088,0.00001233933],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967709,0.0001449411,0.001753001,0.00002868499,0.000009382212,0.000006917145,0.0004519309,0.00003121466,0.0008031915],"genre_scores_gemma":[0.9932235,0.0001209416,0.005340724,0.00001446668,0.00002405178,0.00001658439,0.0008294767,0.000006529686,0.0004235945],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00954362,"threshold_uncertainty_score":0.01897615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05042794509519434,"score_gpt":0.2537726200071242,"score_spread":0.2033446749119299,"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."}}