{"id":"W2324506632","doi":"10.1139/cjce-2013-0090","title":"Development of sustainable irrigation planning with multi-objective fuzzy linear programming for Ukai–Kakrapar irrigation project, Gujarat, India","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water resources management and optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Irrigation; Inflow; Maximization; Water resource management; Linear programming; Environmental science; Mathematics; Hydrology (agriculture); Agricultural engineering; Engineering; Mathematical optimization; Geography; Meteorology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002256657,0.000176759,0.0002134185,0.000676755,0.00009506912,0.00008948435,0.0001344688,0.00006705117,0.00001038854],"category_scores_gemma":[0.00003724224,0.0001680182,0.00004527706,0.0003125024,0.00001687236,0.0005244335,0.000007564492,0.0001502698,0.000001184896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002734901,"about_ca_system_score_gemma":0.0002265908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002058426,"about_ca_topic_score_gemma":0.0021941,"domain_scores_codex":[0.9989331,0.000007528582,0.000421486,0.000101155,0.0001431615,0.00039353],"domain_scores_gemma":[0.9992885,0.00002047571,0.0001582209,0.00008037637,0.0002800217,0.000172406],"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":[0.000008096273,0.000007525785,0.0009030407,0.0004205748,0.0001421916,0.00001392705,0.007259737,0.9893669,0.0004053941,0.0002013377,0.0001414064,0.001129849],"study_design_scores_gemma":[0.003235151,0.0004181395,0.01813675,0.001431491,0.0001784032,0.00004717358,0.009077133,0.9178762,0.01150137,0.0001296055,0.03681037,0.001158149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3154952,0.0003753895,0.6808518,0.00001831748,0.0002465608,0.001588022,0.000004065053,0.00009337249,0.001327301],"genre_scores_gemma":[0.9060311,0.000001223683,0.09365751,0.000004128299,0.00008772867,0.00007127265,0.00002186148,0.00005073073,0.0000744756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5905359,"threshold_uncertainty_score":0.6851583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01079002016923288,"score_gpt":0.197450854607656,"score_spread":0.1866608344384231,"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."}}