{"id":"W2623979951","doi":"10.3390/w9060414","title":"Inexact Two-Stage Stochastic Programming for Water Resources Allocation under Considering Demand Uncertainties and Response—A Case Study of Tianjin, China","year":2017,"lang":"en","type":"article","venue":"Water","topic":"Water resources management and optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Stochastic programming; Interval (graph theory); Water scarcity; Time horizon; Stage (stratigraphy); Economic shortage; Resource (disambiguation); Water resources; Operations research; Water resource management; Environmental science; Computer science; Water supply; Mathematical optimization; Environmental economics; Environmental engineering; Economics; Engineering; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003835334,0.0001552815,0.0001778292,0.0001232657,0.0003048895,0.0002722192,0.0001046651,0.00003822079,0.00001108268],"category_scores_gemma":[0.00001545167,0.0001035691,0.00002626363,0.00001525111,0.00005851022,0.0002468993,0.0001049322,0.00005638922,0.000001729959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002080893,"about_ca_system_score_gemma":0.000001435836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002934848,"about_ca_topic_score_gemma":0.0002589066,"domain_scores_codex":[0.9992102,0.00003827546,0.0002294706,0.0001807679,0.00009680695,0.0002444403],"domain_scores_gemma":[0.9995742,0.00002730949,0.00004436542,0.0002840674,0.00003343421,0.00003657786],"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":[0.0002955149,0.00005433109,0.001512675,0.0002686456,0.0001703267,0.0000665308,0.04707033,0.944536,0.005104017,0.000008606876,0.00002065026,0.0008923296],"study_design_scores_gemma":[0.01083236,0.001280256,0.00582549,0.0002422401,0.00057244,0.0001605637,0.03341983,0.8840743,0.05702984,0.000406345,0.00475801,0.001398293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9794424,0.00002786606,0.01947221,0.00008634506,0.00008740131,0.0007594642,0.000002234706,0.00008376903,0.00003835277],"genre_scores_gemma":[0.9989424,0.00000105446,0.0004158197,0.000005201613,0.00004366758,0.00006501533,0.00001241368,0.00003587768,0.0004785414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06046171,"threshold_uncertainty_score":0.4223427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02108889815646245,"score_gpt":0.2530170329299015,"score_spread":0.2319281347734391,"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."}}