{"id":"W2766303556","doi":"10.1016/j.scitotenv.2017.10.068","title":"Impacts of rapid urbanization on the water quality and macroinvertebrate communities of streams: A case study in Liangjiang New Area, China","year":2017,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Freshwater macroinvertebrate diversity and ecology","field":"Environmental Science","cited_by":138,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"China National Critical Project for Science and Technology on Water Pollution Prevention and Control","keywords":"Urbanization; Species richness; Water quality; Ecology; Environmental science; Geography; Habitat; Abundance (ecology); Biotic index; Nutrient; Ecosystem; Chironomidae; Biology","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":[0.000530281,0.0003142923,0.0002540903,0.001019401,0.001461009,0.0006531906,0.0005939923,0.0004419595,0.0006710566],"category_scores_gemma":[0.0006323623,0.0002392467,0.0005308145,0.001656187,0.00132158,0.0005406273,0.001038727,0.0003060338,0.00004233537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004520335,"about_ca_system_score_gemma":0.002960308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2368679,"about_ca_topic_score_gemma":0.4941601,"domain_scores_codex":[0.9995174,0.0001282141,0.00003042819,0.0000642115,0.0000737886,0.0001859816],"domain_scores_gemma":[0.9995133,0.00009585675,0.00012803,0.00002693878,0.00007289978,0.0001629416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009823237,0.0002624896,0.9789273,0.00003724556,0.00008712056,0.006681668,0.005278693,0.001132238,0.001650735,0.0003311735,0.0001957523,0.005317444],"study_design_scores_gemma":[0.000009786168,0.0001368481,0.9806429,0.000011096,0.00005252903,0.0005579057,0.01595113,0.001853621,0.0002402367,0.0001325249,0.0003959745,0.00001543985],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997569,0.00001250814,0.00002246266,0.00002209261,6.84543e-7,0.00000454074,0.00002032276,9.524499e-7,0.0001596277],"genre_scores_gemma":[0.9996868,0.00005123103,0.00005754086,0.000009862347,0.000001640694,0.000004621565,0.00003341487,5.082749e-7,0.0001542712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2368679,"threshold_uncertainty_score":0.4709783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03238489331368595,"score_gpt":0.235863877754601,"score_spread":0.203478984440915,"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."}}