{"id":"W2800738505","doi":"10.1111/1752-1688.12643","title":"A Water Allocation Decision‐Support Model and Tool for Predictions in Ungauged Basins in Northeast British Columbia, Canada","year":2018,"lang":"en","type":"article","venue":"JAWRA Journal of the American Water Resources Association","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Forests; Geoscience BC","funders":"Ministry of Forests, Lands and Natural Resource Operations; Government of the United Kingdom","keywords":"Surface runoff; Environmental science; Water balance; Watershed; Hydrology (agriculture); Statistic; Water resources; Decision support system; Drainage basin; Statistics; Computer science; Geography; Mathematics; 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":[0.0006162376,0.0006223732,0.0003444531,0.000693164,0.001312263,0.00129028,0.001153007,0.000556712,0.003406983],"category_scores_gemma":[0.001519019,0.0004136271,0.0002650993,0.0007511345,0.0005089568,0.0005045158,0.0004204714,0.0004560572,0.0002608877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01779897,"about_ca_system_score_gemma":0.01513636,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9459113,"about_ca_topic_score_gemma":0.9365712,"domain_scores_codex":[0.9998255,0.00003709447,0.00001005347,0.00004242993,0.00003773638,0.00004715991],"domain_scores_gemma":[0.9993359,0.0002340592,0.00002906497,0.00002018855,0.0003071139,0.0000736031],"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":[0.00006466517,0.00007544566,0.01396973,0.00001795916,0.00001487343,0.00009108758,0.00005994962,0.9741799,0.0002743062,0.000696276,0.001842772,0.008712884],"study_design_scores_gemma":[0.00001227367,0.000005515994,0.001153663,0.000002642146,0.00000317996,0.000002193186,0.00004095749,0.9981821,0.000119928,0.0001221606,0.0003511337,0.000004319144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959901,0.00008173093,0.02311507,0.0005650009,0.00002310102,0.0002559972,0.003324572,0.001356574,0.01137703],"genre_scores_gemma":[0.9832715,0.00005230967,0.01160111,0.00003093366,0.000003424846,0.0001105293,0.001219919,0.00003805185,0.003672206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05408871,"threshold_uncertainty_score":0.1291412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004224414095871504,"score_gpt":0.1934675610079299,"score_spread":0.1892431469120584,"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."}}