{"id":"W4220738622","doi":"10.1016/j.jenvman.2022.114946","title":"An improved fuzzy sorting algorithm coupling bi-level programming for synergetic optimization of agricultural water resources: A case study of Fujian Province, China","year":2022,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Water resources management and optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Fujian Provincial Department of Science and Technology","keywords":"Sorting; Agriculture; Water resources; Fuzzy logic; Ranking (information retrieval); Farm water; Environmental economics; Computer science; Mathematical optimization; Agricultural engineering; Water resource management; Operations research; Environmental science; Water conservation; Mathematics; Engineering; Economics; Geography; Algorithm","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":[0.000461551,0.0001938606,0.0002819141,0.0002730541,0.0001864053,0.00004422391,0.0002432297,0.00002502078,0.00002946961],"category_scores_gemma":[0.000001679428,0.0001534598,0.0001082045,0.000124185,0.00002353138,0.0002816308,0.0001855071,0.0001317142,1.529318e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001786538,"about_ca_system_score_gemma":0.000001653688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001851014,"about_ca_topic_score_gemma":0.000004487791,"domain_scores_codex":[0.9983246,0.00003929861,0.0008200977,0.0001775421,0.0003891416,0.0002492641],"domain_scores_gemma":[0.9993141,0.000008537773,0.0004349611,0.0001675906,0.00001434734,0.00006044342],"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":[0.00004476584,0.00076075,0.0003680261,0.0001941309,0.0002857409,0.00009918076,0.004219348,0.9809467,0.00217525,0.000001505221,0.00000732721,0.01089729],"study_design_scores_gemma":[0.002868261,0.002496653,0.00221396,0.00004861458,0.000525241,0.0001185337,0.06299701,0.9270903,0.001092361,0.000008959711,0.0002060422,0.0003340787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9435847,0.00006295778,0.05483382,0.00000847136,0.0001358018,0.001286942,0.00001368107,0.00002601729,0.00004758871],"genre_scores_gemma":[0.9648923,0.0000144582,0.03478795,0.000003269661,0.00005576437,0.00006937236,0.00003587298,0.00004053301,0.0001004692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05877766,"threshold_uncertainty_score":0.625791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006894089708514566,"score_gpt":0.1888528473962512,"score_spread":0.1819587576877366,"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."}}