{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003968148,0.00006822813,0.0001271704,0.0002404412,0.00003662859,0.00006142964,0.0001360258,0.00001893671,0.000125187],"category_scores_gemma":[0.000005438281,0.00003596496,0.00001634599,0.00003763639,0.00005650969,0.0002841707,0.00004831392,0.0001653852,9.817346e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001594382,"about_ca_system_score_gemma":0.00000826319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001495929,"about_ca_topic_score_gemma":0.00001458671,"domain_scores_codex":[0.9987817,0.00006989214,0.0002699301,0.00007241665,0.0005897297,0.0002163017],"domain_scores_gemma":[0.9997619,0.000007102791,0.0000326392,0.00003271276,0.00002514353,0.000140531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005399805,0.00440409,0.1406435,0.00266962,0.002480615,0.0005093198,0.28216,0.209219,0.2008974,0.001126684,0.002405658,0.1480843],"study_design_scores_gemma":[0.02117112,0.01269131,0.1927346,0.001637835,0.00003502419,0.0001780119,0.008942519,0.2553045,0.392136,0.002987227,0.1107332,0.00144874],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885883,0.0001534687,0.005314201,0.005591147,0.00004132552,0.0001252418,0.000006730905,0.0000054715,0.0001741264],"genre_scores_gemma":[0.9993648,0.0003884812,0.00007578284,0.00004001814,0.00007548257,0.000002574358,0.00002114877,0.000009465627,0.00002225042],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2732175,"threshold_uncertainty_score":0.1466609,"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."}}