{"id":"W2114215403","doi":"10.1007/s10661-008-0301-y","title":"Assessment of surface water quality using multivariate statistical techniques in red soil hilly region: a case study of Xiangjiang watershed, China","year":2008,"lang":"en","type":"article","venue":"Environmental Monitoring and Assessment","topic":"Water Quality and Pollution Assessment","field":"Environmental Science","cited_by":188,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Principal component analysis; Watershed; Water quality; Multivariate statistics; Environmental science; Sampling (signal processing); Multivariate analysis; Cluster (spacecraft); Hydrology (agriculture); Surface water; Statistics; Environmental engineering; Mathematics; Computer science; Geology; Ecology","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":[0.0009565677,0.0003795378,0.0003727493,0.001441722,0.0005971267,0.0006533624,0.0005715821,0.0003268789,0.000220924],"category_scores_gemma":[0.0008699591,0.0002080788,0.0004485451,0.001846978,0.000601702,0.0004649362,0.0003983794,0.0001354347,0.00002453356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001621036,"about_ca_system_score_gemma":0.001240966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1094474,"about_ca_topic_score_gemma":0.1312224,"domain_scores_codex":[0.9995521,0.0001329237,0.0000300455,0.00007736278,0.0001292057,0.00007841705],"domain_scores_gemma":[0.9995103,0.0001749932,0.0001040504,0.00003842526,0.0001159675,0.00005637691],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002509043,0.0003517363,0.9218397,0.00007379161,0.000164564,0.00217421,0.002021187,0.02064644,0.01503633,0.0005749281,0.0001838777,0.03668243],"study_design_scores_gemma":[0.00001710302,0.0002348612,0.9200745,0.00000600057,0.0001005095,0.0002162675,0.002688156,0.07243195,0.003699717,0.0002434844,0.0002603718,0.00002706786],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994362,0.00001530922,0.0003682636,0.000009213678,3.267795e-7,0.000005699458,0.00002376106,0.000004927627,0.0001362584],"genre_scores_gemma":[0.9990277,0.00002901223,0.0007740879,0.000002109261,0.000001225994,0.000003262195,0.00003591847,0.000001183717,0.0001255322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1094474,"threshold_uncertainty_score":0.2176208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05969927884440818,"score_gpt":0.3529891197540114,"score_spread":0.2932898409096032,"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."}}