{"id":"W2165596007","doi":"10.5194/hess-18-1605-2014","title":"Comprehensive evaluation of water resources security in the Yellow River basin based on a fuzzy multi-attribute decision analysis approach","year":2014,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Water Resources and Sustainability","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"National Key Research and Development Program of China; National Science Foundation","keywords":"TOPSIS; Ranking (information retrieval); Ideal solution; Water resources; Computer science; Structural basin; Fuzzy logic; Water security; Multiple-criteria decision analysis; Rank (graph theory); Data mining; Water resource management; Operations research; Environmental science; Mathematics; Geology; Machine learning; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003650065,0.0007954229,0.0007528612,0.004827004,0.0009265278,0.002113717,0.0004914058,0.0005125052,0.000981979],"category_scores_gemma":[0.002832756,0.000322929,0.001253552,0.002284117,0.0005781661,0.001488212,0.001049341,0.0004886959,0.00005199916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002653453,"about_ca_system_score_gemma":0.002716841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009754962,"about_ca_topic_score_gemma":0.01170885,"domain_scores_codex":[0.997833,0.0009119873,0.0001845549,0.0001876769,0.0007363387,0.0001464211],"domain_scores_gemma":[0.998903,0.0004316728,0.0001126714,0.00004767124,0.0004072141,0.00009767018],"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":[0.0003075375,0.0002976343,0.05427356,0.0005166777,0.000726079,0.0005679603,0.0007864964,0.7554281,0.01162846,0.02054468,0.001318186,0.1536046],"study_design_scores_gemma":[0.00002279635,0.0001404556,0.009123493,0.00005408176,0.0001147029,0.00005608665,0.0004482644,0.9794999,0.0020436,0.0076172,0.0008393112,0.00003999897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4866001,0.0008990926,0.5043716,0.0004920733,0.00004833689,0.000443922,0.0003876736,0.0001400534,0.00661712],"genre_scores_gemma":[0.9437567,0.000166461,0.05525836,0.00002283618,0.000008499578,0.0001331839,0.0001021898,0.000004685741,0.000547022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009754962,"threshold_uncertainty_score":0.01939636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02237542033228465,"score_gpt":0.2490405329462151,"score_spread":0.2266651126139304,"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."}}