{"id":"W2904277277","doi":"10.1051/matecconf/201824602048","title":"Optimization of water flushing in lowland urban river in Jiaxing, Zhejiang using dissolved oxygen as the indicator","year":2018,"lang":"en","type":"article","venue":"MATEC Web of Conferences","topic":"Aquatic Ecosystems and Phytoplankton Dynamics","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Flushing; Environmental science; Saturation (graph theory); Water quality; Oxygen saturation; Hydrology (agriculture); Biochemical oxygen demand; Duration (music); Oxygen; Environmental engineering; Chemical oxygen demand; Chemistry; Ecology; Geology; Biology; Mathematics; Wastewater","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":[0.0002738222,0.0002669038,0.0004071918,0.0002463334,0.0004971427,0.0005139798,0.0002379259,0.0002303598,0.0002628884],"category_scores_gemma":[0.0002136257,0.0001334583,0.000302846,0.0003155515,0.0001955354,0.0001789049,0.0002153989,0.000153575,0.00003960745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006653288,"about_ca_system_score_gemma":0.0005494703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0180736,"about_ca_topic_score_gemma":0.03978911,"domain_scores_codex":[0.999874,0.00002062636,0.00001276922,0.00003251841,0.00002755701,0.00003258478],"domain_scores_gemma":[0.9999208,0.00001664657,0.00002072659,0.000004195575,0.00001915416,0.00001837546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00126055,0.0005143615,0.1592448,0.0007212483,0.000142524,0.0007104825,0.0007743515,0.05208326,0.707344,0.0002857887,0.0004450906,0.07647362],"study_design_scores_gemma":[0.0001045951,0.002842414,0.7493867,0.00003177123,0.0002151479,0.0001216344,0.001590759,0.07138413,0.1709921,0.0002141169,0.003045423,0.00007130784],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989933,0.00008053259,0.0006862566,0.00001320802,0.000003034903,0.00001112116,0.00002959949,0.00001079956,0.0001722419],"genre_scores_gemma":[0.9986882,0.00007814004,0.000825713,0.000006144812,0.000001081259,0.00001141876,0.00006950032,0.000002491464,0.0003174029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0180736,"threshold_uncertainty_score":0.03593683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01178610157209571,"score_gpt":0.2264887860611758,"score_spread":0.2147026844890801,"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."}}