{"id":"W2522395321","doi":"10.5194/essd-9-221-2017","title":"A high space–time resolution dataset linking meteorological forcing and hydro-sedimentary response in a mesoscale Mediterranean catchment (Auzon) of the Ardèche region, France","year":2017,"lang":"en","type":"article","venue":"Earth system science data","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Amec Foster Wheeler (Canada)","funders":"Institut national des sciences de l'Univers; Centre National de la Recherche Scientifique; Réseau des Bassins Versants; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Agence Nationale de la Recherche; National Science Foundation","keywords":"Hydrometeorology; Environmental science; Flash flood; Mesoscale meteorology; Snowmelt; Surface runoff; Drainage basin; Mediterranean climate; Hydrology (agriculture); Precipitation; Meteorology; Snow; Climatology; Geology; Geography; Flood myth","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004623421,0.0004788193,0.000451951,0.001419888,0.0002105465,0.0006777401,0.000542709,0.000563865,0.0021933],"category_scores_gemma":[0.0008059945,0.0001673341,0.0004510969,0.001390818,0.000228403,0.0002823588,0.0004892694,0.0002876518,0.0006334115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001139203,"about_ca_system_score_gemma":0.0007886431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1688365,"about_ca_topic_score_gemma":0.1821377,"domain_scores_codex":[0.9997485,0.00004070517,0.00001599535,0.00009396425,0.00005917023,0.00004175153],"domain_scores_gemma":[0.9996456,0.00005415927,0.0000706116,0.0000604603,0.0001077381,0.00006143795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001376759,0.0008198502,0.6335666,0.00112391,0.001492333,0.001872219,0.001043087,0.08031144,0.01880793,0.001638982,0.1624479,0.09549892],"study_design_scores_gemma":[0.000232426,0.00005644478,0.9343559,0.00007108081,0.00003174456,0.00009086217,0.0002166921,0.01630992,0.0005983205,0.0001750656,0.0478298,0.00003182],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.6453569,0.0008173318,0.00120315,0.0003877584,0.00006505151,0.0001194032,0.3490252,0.0008563154,0.002168808],"genre_scores_gemma":[0.5599487,0.0003377331,0.005121965,0.000102236,0.00007936047,0.0002322846,0.431674,0.00008545511,0.002418243],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.1688365,"threshold_uncertainty_score":0.3357075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02783063571085997,"score_gpt":0.2536465629306849,"score_spread":0.2258159272198249,"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."}}