{"id":"W4407204916","doi":"10.1016/j.watres.2025.123250","title":"CO2 agitation combined with magnetized biochar to alleviate “ammonia inhibited steady-state”: Exploring the mechanism by combining metagenomics with macroscopic indicators","year":2025,"lang":"en","type":"article","venue":"Water Research","topic":"Microbial Fuel Cells and Bioremediation","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"China Postdoctoral Science Foundation; Postdoctoral Research Foundation of China; National Natural Science Foundation of China","keywords":"Biochar; Mechanism (biology); Metagenomics; Ammonia; Steady state (chemistry); Chemistry; Computational biology; Biochemical engineering; Environmental chemistry; Biology; Biochemistry; Physics; Gene; Engineering; Organic chemistry","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":[0.0001285383,0.0003477416,0.0002436123,0.000114621,0.0001465334,0.0003658825,0.0002918741,0.0003994733,0.0004202616],"category_scores_gemma":[0.0001529933,0.0001241532,0.0002275301,0.0001136454,0.0002704598,0.0003238468,0.0001870709,0.0003720218,0.0001243641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003319992,"about_ca_system_score_gemma":0.0002686357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001033945,"about_ca_topic_score_gemma":0.001963827,"domain_scores_codex":[0.9998869,0.0000110945,0.000006131003,0.00003379953,0.00002878899,0.00003335425],"domain_scores_gemma":[0.9999415,0.00001033488,0.00001663428,0.000006356907,0.00001547063,0.000009696215],"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":[0.00007803981,0.000009764792,0.00006517125,0.00002026127,0.000002885781,0.00001246081,0.000005092602,0.00005291972,0.998955,0.0000439537,0.0000273295,0.0007271447],"study_design_scores_gemma":[0.000002265437,0.0000356995,0.0002817428,7.996358e-7,0.00000273653,0.00000609163,0.000007388933,0.0007856681,0.9986627,0.00001449095,0.000198412,0.000002055728],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895706,0.0007976169,0.007827333,0.0002359213,0.0001010987,0.00003727169,0.0001818922,0.0001806093,0.001067699],"genre_scores_gemma":[0.9959335,0.0002281262,0.002837033,0.00004974433,0.000009378612,0.00001490677,0.0001028414,0.00001612632,0.0008084241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001033945,"threshold_uncertainty_score":0.002408803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02509806325016987,"score_gpt":0.2637954558978606,"score_spread":0.2386973926476908,"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."}}