{"id":"W2055437633","doi":"10.1007/s12665-009-0303-2","title":"Water quality assessment of Wei River, China using fuzzy synthetic evaluation","year":2009,"lang":"en","type":"article","venue":"Environmental Earth Sciences","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Water quality; Environmental science; Pollutant; Water resource management; Mercury (programming language); Sampling (signal processing); Hydrology (agriculture); Agriculture; Water body; Wetland; Wastewater; Environmental engineering; China; Geography; Ecology; Geology; Engineering","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":[0.001170351,0.0004550772,0.0004994817,0.001955172,0.0008282334,0.001211772,0.0003742137,0.0004535267,0.000359648],"category_scores_gemma":[0.001342531,0.0002354129,0.0006735746,0.001421323,0.0004772355,0.000656964,0.0004843883,0.0001355428,0.00002476403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001856391,"about_ca_system_score_gemma":0.001389699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03212821,"about_ca_topic_score_gemma":0.02743419,"domain_scores_codex":[0.9994003,0.0001665969,0.00005823715,0.00008245322,0.0002298597,0.00006267113],"domain_scores_gemma":[0.9994405,0.0001499634,0.00007184268,0.00003133436,0.0002543588,0.00005201035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0007853105,0.0002530635,0.07888418,0.0002439951,0.0002682754,0.0004296661,0.000474878,0.8162579,0.02738428,0.004789473,0.0008462201,0.06938279],"study_design_scores_gemma":[0.00003185779,0.0001165292,0.01859533,0.000006392135,0.00007109624,0.00002699101,0.000201066,0.9753163,0.003806023,0.001476387,0.0003204267,0.00003146454],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9836723,0.00006376627,0.01471701,0.00005490746,0.000006665949,0.00002261986,0.0001141582,0.0000347114,0.001313807],"genre_scores_gemma":[0.9976312,0.00002018439,0.002104388,0.000003352605,0.000001822905,0.000007731494,0.00007028046,0.000002251468,0.0001587343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03212821,"threshold_uncertainty_score":0.06388241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05697216324724428,"score_gpt":0.342270134048058,"score_spread":0.2852979708008136,"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."}}