{"id":"W4316036656","doi":"10.1175/jhm-d-22-0098.1","title":"Methods for Estimating Surface Water Storage Changes and Their Evaluations","year":2023,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada","funders":"Natural Resources Canada; Government of Canada","keywords":"Environmental science; Surface runoff; Water resources; Water storage; Surface water; Hydrology (agriculture); Climate change; Snow; Glacier; Water cycle; Scale (ratio); Climatology; Water resource management; Physical geography; Meteorology; Geography; Geology; Environmental engineering; Inlet; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.005583962,0.0009969999,0.0005423783,0.003409036,0.0002838027,0.001285621,0.0008803064,0.0006584498,0.003039855],"category_scores_gemma":[0.0140752,0.0004202274,0.000728345,0.00158564,0.0006082971,0.001515147,0.001136247,0.0007197229,0.0007093421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008587565,"about_ca_system_score_gemma":0.0007743805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004090895,"about_ca_topic_score_gemma":0.003103025,"domain_scores_codex":[0.9968167,0.001279007,0.0002044966,0.0003369142,0.001293339,0.00006954648],"domain_scores_gemma":[0.9913425,0.005078941,0.0006509025,0.0006604432,0.002201071,0.00006603801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002188712,0.0001091907,0.01745766,0.0005538524,0.0002626566,0.00007843706,0.0001856691,0.2380626,0.01140676,0.02273662,0.003521361,0.7054064],"study_design_scores_gemma":[0.00002472429,0.00009188683,0.005260261,0.00009412626,0.000030675,0.00007239493,0.00006424599,0.9729772,0.008730798,0.008970728,0.003635086,0.00004785004],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01303717,0.0005207492,0.982981,0.0000783126,0.00004065626,0.0001751482,0.0003523323,0.0005795976,0.002235044],"genre_scores_gemma":[0.3049512,0.0009631056,0.6879591,0.00005994193,0.00008651813,0.001117128,0.0008258036,0.0002870955,0.003750015],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005583962,"threshold_uncertainty_score":0.02953112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03688610705292381,"score_gpt":0.3449599021638773,"score_spread":0.3080737951109535,"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."}}