{"id":"W2993360861","doi":"10.3390/su11247027","title":"China’s Agricultural Irrigation and Water Conservancy Projects: A Policy Synthesis and Discussion of Emerging Issues","year":2019,"lang":"en","type":"article","venue":"Sustainability","topic":"Water-Energy-Food Nexus Studies","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Institute for Water Security; University of Saskatchewan","funders":"National Key Research and Development Program of China; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering; National Natural Science Foundation of China; University of Saskatchewan","keywords":"Business; Sustainable development; Subsidy; Sustainability; Agriculture; China; Incentive; Environmental planning; Natural resource economics; Environmental resource management; Economics; Political science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002207914,0.0001361564,0.0001944836,0.00003150551,0.0001253493,0.00001569599,0.00007122161,0.00004302861,0.00005410366],"category_scores_gemma":[0.0002204555,0.00006451201,0.00002790759,0.0001137556,0.0002863192,0.0003034432,0.0003979185,0.00004810879,0.000003202424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002516455,"about_ca_system_score_gemma":0.00001370177,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007083041,"about_ca_topic_score_gemma":0.000209507,"domain_scores_codex":[0.9989734,0.0001050101,0.0001805557,0.0003219151,0.0001588632,0.0002602226],"domain_scores_gemma":[0.9996594,0.00002430163,0.00005036458,0.0001836338,0.00003348249,0.00004886291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001914225,0.0003238241,0.81556,0.001513186,0.00006967397,0.000005983587,0.04732084,0.0001223254,0.08939262,0.0201194,0.000297031,0.02508368],"study_design_scores_gemma":[0.0001378442,0.00007928521,0.8865908,0.00002149316,0.00001245881,0.000003000858,0.006219277,0.00005615146,0.02933749,0.07719184,0.0002152065,0.0001351765],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839179,0.0000468974,0.000003022336,0.01301421,0.00002238036,0.0003422117,0.000005305116,0.0000241416,0.002623935],"genre_scores_gemma":[0.9987079,0.00000537903,0.0001001211,0.0000181798,0.00001651064,0.00003074025,0.00000231945,0.000006244171,0.001112588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07103077,"threshold_uncertainty_score":0.9995289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004481741235807679,"score_gpt":0.2278150726290137,"score_spread":0.223333331393206,"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."}}