{"id":"W4408436202","doi":"10.5194/egusphere-egu25-7377","title":"Toward a global scale runoff estimation through satellite observations: the STREAM model&amp;#160;","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Environmental science; Surface runoff; Hydrology (agriculture); Discharge; Streamflow; Context (archaeology); Drainage basin; Precipitation; Snowmelt; Flood myth; Water resources; Climate change; Geology; Meteorology; Geography; Oceanography; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003992312,0.0004532959,0.0004217562,0.0003437678,0.0002128819,0.0008451629,0.0008011307,0.0009033267,0.001006394],"category_scores_gemma":[0.001006616,0.0002984298,0.0005945261,0.001182505,0.0003994086,0.001011843,0.0005936153,0.0007405854,0.0003910275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000688343,"about_ca_system_score_gemma":0.001182411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03689508,"about_ca_topic_score_gemma":0.02662867,"domain_scores_codex":[0.999876,0.00003769282,0.000004939236,0.00004070562,0.00002940331,0.0000111845],"domain_scores_gemma":[0.9998345,0.00005422977,0.00002221755,0.00002769939,0.00004568147,0.00001572382],"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":[0.00001282606,0.00002281678,0.002313548,0.00001453822,0.00002069193,0.00001452312,0.00001363129,0.9843931,0.0005985873,0.001572865,0.0009546724,0.01006825],"study_design_scores_gemma":[0.000007327846,0.000005159655,0.0004370898,0.000002905854,0.000004291433,0.000002217008,0.000004231606,0.9979839,0.0001431834,0.0006667752,0.0007391446,0.000003622762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2742859,0.0006033817,0.6990582,0.002096806,0.0001818395,0.0001756673,0.007767902,0.003519439,0.01231084],"genre_scores_gemma":[0.8276827,0.001204156,0.1590109,0.0003361244,0.0001143026,0.0002785396,0.005104581,0.0003901191,0.005878649],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03689508,"threshold_uncertainty_score":0.07336062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1057611779391911,"score_gpt":0.2919576089281876,"score_spread":0.1861964309889965,"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."}}