{"id":"W3009907330","doi":"10.3390/ijerph17051730","title":"Standardization of Exchanged Water with Different Properties in China’s Water Rights Trading","year":2020,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Water resources management and optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Water trading; Standardization; Value (mathematics); Business; Transaction cost; Water right; Shadow price; Database transaction; Water use; Water quality; Environmental economics; China; Water resources; Natural resource economics; Water conservation; Economics; Computer science; Finance; Law; Mathematics","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":[0.004106515,0.0003172189,0.0003609751,0.001229253,0.001024851,0.002743803,0.0009054276,0.0007479168,0.001622557],"category_scores_gemma":[0.005387597,0.000249694,0.0007121796,0.002170518,0.002649999,0.004407666,0.002052024,0.0006860972,0.00009347693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005201153,"about_ca_system_score_gemma":0.00673379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0240097,"about_ca_topic_score_gemma":0.01679156,"domain_scores_codex":[0.9952191,0.001049819,0.0004152048,0.000604902,0.002214151,0.0004968517],"domain_scores_gemma":[0.998499,0.0003239508,0.0003232635,0.0003433111,0.0004284723,0.00008190861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002210534,0.0002383246,0.1208026,0.0004654157,0.0001332551,0.001650701,0.003791734,0.1230143,0.01848736,0.4400318,0.004846677,0.2863169],"study_design_scores_gemma":[0.0002264039,0.0005836035,0.189959,0.0002876675,0.0002733602,0.0008079095,0.006649701,0.398572,0.03102494,0.2768413,0.0943989,0.0003752643],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8339341,0.0008422534,0.1028757,0.002102965,0.0001176914,0.000448056,0.000277058,0.0001849525,0.05921729],"genre_scores_gemma":[0.99193,0.0001749064,0.005448577,0.00005334022,0.00001057035,0.00005608782,0.00009208949,0.00001348773,0.002220873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0240097,"threshold_uncertainty_score":0.04773992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04485138220506957,"score_gpt":0.2584009916909007,"score_spread":0.2135496094858311,"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."}}