{"id":"W2795601653","doi":"10.1016/j.jclepro.2018.04.028","title":"Planning a sustainable regional irrigated production and forest protection under land and water stresses with multiple uncertainties","year":2018,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Water resources management and optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Beijing Social Science Fund; National Natural Science Foundation of China","keywords":"Environmental science; Sustainability; Environmental resource management; Robustness (evolution); Water resources; Production (economics); Vagueness; Forest protection; Environmental economics; Business; Water resource management; Computer science; Forest management; Fuzzy logic; Agroforestry; Economics; Ecology","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":[0.0002102104,0.0001059087,0.0001077832,0.0001964842,0.0001494015,0.00009042044,0.00002982529,0.00003811366,0.00000344265],"category_scores_gemma":[0.00002983645,0.00006913816,0.0000115946,0.00009490769,0.00008838433,0.000656341,0.00001500996,0.0001149727,5.452017e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003955071,"about_ca_system_score_gemma":0.00000534346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002770202,"about_ca_topic_score_gemma":0.00004950609,"domain_scores_codex":[0.9993714,0.00002275287,0.0001739764,0.0001315098,0.0001485723,0.0001517792],"domain_scores_gemma":[0.999573,0.000004151663,0.00008083669,0.00006834117,0.0002404313,0.00003322427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006201006,0.00003076308,0.03279092,0.0003563122,0.0001410444,0.00000785249,0.002475244,0.957109,0.003737511,0.00001829889,0.001058818,0.00165409],"study_design_scores_gemma":[0.006668733,0.005725338,0.2397185,0.002562726,0.001009092,0.003531537,0.02739618,0.2976747,0.3312554,0.005920365,0.07628751,0.002249943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942467,0.0001475661,0.004323339,0.0007329541,0.0001974942,0.000237284,1.207453e-7,0.00005299341,0.00006152235],"genre_scores_gemma":[0.998082,0.00004705784,0.0004165476,0.000006995771,0.000753797,0.00000550628,0.000004048635,0.00002020758,0.0006638751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6594343,"threshold_uncertainty_score":0.2819372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01311758198392795,"score_gpt":0.1928812892193894,"score_spread":0.1797637072354615,"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."}}