{"id":"W2382038818","doi":"","title":"Application of WRMM model toUrumqi river water resource management","year":2005,"lang":"en","type":"article","venue":"Journal of Xinjiang Agricultural University","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Arid; Water resource management; Drainage basin; Irrigation; Environmental science; Water resources; Resource (disambiguation); Channel (broadcasting); Hydrology (agriculture); Drainage; Natural resource; Computer science; Geology; Geography; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008404754,0.00006967342,0.0001047867,0.00008013998,0.00008673842,0.00001161387,0.000555522,0.00003016508,0.000001587848],"category_scores_gemma":[9.4363e-7,0.00004563247,0.00009350291,0.0001914004,0.00002833642,0.0004999157,0.0001490724,0.00009044546,0.000006712763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001110546,"about_ca_system_score_gemma":0.000009185647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002111434,"about_ca_topic_score_gemma":5.360777e-7,"domain_scores_codex":[0.999396,0.0000168044,0.0001744949,0.0001116039,0.0001996332,0.0001014688],"domain_scores_gemma":[0.9993953,0.00001342086,0.0002006488,0.0001340693,0.0001994881,0.00005713709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004348272,0.0003271593,0.00008212767,0.00003461375,0.0001113247,0.00002076663,0.001464707,0.4720872,0.02344884,0.3942221,0.01261312,0.09554464],"study_design_scores_gemma":[0.002238682,0.0003205853,0.02864087,0.0001543143,0.0001811097,0.0002980895,0.001251338,0.3500259,0.04735364,0.02691116,0.5417461,0.0008782185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05421861,0.00001941368,0.9403583,0.002815098,0.0000105851,0.00009304172,0.000002001472,0.00003222377,0.002450743],"genre_scores_gemma":[0.7010009,0.00002073852,0.2978736,0.00006064651,0.00004082577,3.093039e-7,0.00000222891,0.000001733351,0.000999012],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6467823,"threshold_uncertainty_score":0.1860838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005874888829887382,"score_gpt":0.197760184074416,"score_spread":0.1918852952445286,"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."}}