{"id":"W2558013541","doi":"10.1002/fee.1432","title":"Using ecosystem service trade‐offs to inform water conservation policies and management practices","year":2016,"lang":"en","type":"review","venue":"Frontiers in Ecology and the Environment","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":161,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada; National Development and Reform Commission; Washington State University; University of Minnesota; Gordon and Betty Moore Foundation; National Natural Science Foundation of China; State Key Laboratory of Urban and Regional Ecology; Rockefeller Foundation","keywords":"Ecosystem services; Riparian zone; Environmental science; Agriculture; Watershed; Land use; Water quality; Beijing; Water conservation; Ecosystem; Environmental resource management; Business; Water resources; China; Ecology; Geography; Habitat","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002761917,0.001202926,0.001565302,0.004947624,0.000278868,0.001672297,0.001141533,0.001392842,0.00208543],"category_scores_gemma":[0.004275506,0.0002998612,0.001017114,0.00632519,0.0008958858,0.002854497,0.0008388362,0.001094184,0.0003877243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002274449,"about_ca_system_score_gemma":0.002504582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007228762,"about_ca_topic_score_gemma":0.01224516,"domain_scores_codex":[0.9992394,0.0002693033,0.000094524,0.0001305176,0.0002134951,0.0000527153],"domain_scores_gemma":[0.9986162,0.0008412843,0.0002581845,0.00004106669,0.0002029974,0.0000401892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005181578,0.00008664601,0.004469264,0.02551784,0.0007775039,0.0001209971,0.0002180681,0.005751909,0.0007236925,0.03256131,0.007559004,0.9221619],"study_design_scores_gemma":[0.00007357361,0.0003801424,0.03079106,0.04510958,0.002563884,0.001099643,0.002496755,0.009418953,0.002723133,0.1019989,0.8030669,0.0002775096],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001626128,0.9932851,0.001402107,0.0009106501,0.00008549726,0.00001651425,0.0001040509,0.000009517787,0.002560609],"genre_scores_gemma":[0.02488683,0.9724429,0.001904549,0.0001969318,0.00006479904,0.00002731654,0.0001074349,0.000004282258,0.0003649839],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.007228762,"threshold_uncertainty_score":0.01650238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02540531638591158,"score_gpt":0.257539628949069,"score_spread":0.2321343125631574,"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."}}