{"id":"W4251329014","doi":"10.5194/hessd-11-8299-2014","title":"On inclusion of water resource management in Earth System models – Part 2: Representation of water supply and allocation and opportunities for improved modeling","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canada Excellence Research Chairs, Government of Canada","keywords":"Computer science; Scale (ratio); Modular design; Water resources; Resource (disambiguation); Resource allocation; Representation (politics); Key (lock); Resource management (computing); Groundwater; Temporal scales; Environmental science; Distributed computing; Engineering; Geography; Ecology","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":[0.002616365,0.0008044151,0.0009821591,0.0005665465,0.0004784256,0.002653462,0.002041801,0.001540176,0.002724121],"category_scores_gemma":[0.008237141,0.0005620155,0.00134751,0.0008568427,0.001486532,0.005695291,0.002023956,0.00333088,0.0005006877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00134233,"about_ca_system_score_gemma":0.001881601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01188332,"about_ca_topic_score_gemma":0.008203042,"domain_scores_codex":[0.9989679,0.0005723318,0.00006112312,0.0001248091,0.0002134882,0.00006033605],"domain_scores_gemma":[0.9967189,0.00164637,0.0003027662,0.0007766974,0.0004339561,0.0001212981],"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":[0.0000221275,0.00003590079,0.001408264,0.00006766766,0.00004610632,0.00003291502,0.00007028933,0.8870936,0.0007299719,0.0880793,0.00123086,0.02118307],"study_design_scores_gemma":[0.00000527479,0.00001162592,0.0001822147,0.00002399706,0.000008750392,0.000006094839,0.0000151653,0.9690982,0.0003065511,0.02761736,0.002716126,0.000008596232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01987953,0.0007233742,0.9690316,0.002174119,0.0001474536,0.0001009189,0.0004116117,0.0005629772,0.006968421],"genre_scores_gemma":[0.5542597,0.001775156,0.4367301,0.0006375192,0.0003048111,0.0004342133,0.0007996823,0.0005209973,0.004537862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01188332,"threshold_uncertainty_score":0.02362829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03859640717336821,"score_gpt":0.2387979815225868,"score_spread":0.2002015743492186,"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."}}