{"id":"W4413164285","doi":"10.1021/acsestwater.5c00653","title":"Enhanced Denitrification and Microbial Mechanism in Secondary Effluent Treatment Using Combined Iron–Carbon Microelectrolysis and Deep Bed Filters","year":2025,"lang":"en","type":"article","venue":"ACS ES&T Water","topic":"Environmental remediation with nanomaterials","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Foods and Materials Network","funders":"National Natural Science Foundation of China; Science and Technology Foundation of Shenzhen City","keywords":"Effluent; Denitrification; Carbon fibers; Environmental science; Environmental chemistry; Mechanism (biology); Environmental engineering; Waste management; Chemistry; Pulp and paper industry; Materials science; Engineering; Nitrogen; Organic chemistry; Composite material","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":[0.00007167094,0.0001720037,0.000206178,0.000155568,0.00003682713,0.00003960164,0.00004635068,0.00008874381,0.00001923863],"category_scores_gemma":[0.00000330082,0.0001491749,0.00001898397,0.00005930621,0.00002507089,0.00007813842,0.00003160191,0.00005009853,0.000003830547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002653653,"about_ca_system_score_gemma":0.000006879502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004357301,"about_ca_topic_score_gemma":0.00004325445,"domain_scores_codex":[0.999225,0.00003765935,0.0002471545,0.0002146737,0.00005346966,0.0002220319],"domain_scores_gemma":[0.9997832,0.00002121943,0.00002419646,0.0001310124,0.000005126195,0.00003523594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003592395,0.00002184586,0.0003733686,0.00003882679,0.00003886677,0.00000198569,0.0004895437,0.0001598063,0.9973195,0.00001305127,0.000001190258,0.00150614],"study_design_scores_gemma":[0.001563289,0.00004516908,0.002299602,0.00002022939,0.00005261059,0.000002384471,0.00002757241,0.001472777,0.9942315,0.0001215454,0.00002307604,0.0001402544],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988347,0.0001988174,0.000345719,0.00006412684,0.0001358592,0.0003331747,0.000005447183,0.0000450282,0.00003712556],"genre_scores_gemma":[0.999014,0.0002112047,0.0005490062,0.00005279223,0.00001494724,0.00004252365,0.00005171442,0.00002171466,0.00004205605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00308796,"threshold_uncertainty_score":0.6083178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003899950368666436,"score_gpt":0.1862355453903651,"score_spread":0.1823355950216987,"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."}}