{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001217863,0.0001741682,0.0001720594,0.0001957637,0.0004473168,0.0002157948,0.0003877161,0.00004440185,0.0002636533],"category_scores_gemma":[0.00001978175,0.0000931367,0.00002210364,0.000837756,0.0002087512,0.0001701512,0.0003808736,0.0003036239,0.0004240758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001885262,"about_ca_system_score_gemma":0.0000248537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008185403,"about_ca_topic_score_gemma":0.0004887022,"domain_scores_codex":[0.997898,0.0002547585,0.0002323686,0.0004317711,0.0005981204,0.0005849494],"domain_scores_gemma":[0.9994323,0.0000431419,0.00004220782,0.0003049952,0.00005324625,0.0001241048],"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":[0.000464603,0.00007459474,0.001846842,0.00003333181,0.00003542638,0.000007235877,0.004128033,0.0001648483,0.9902421,0.00006511204,0.001467604,0.001470261],"study_design_scores_gemma":[0.001492203,0.0007991248,0.001599527,0.00005874618,0.000018009,9.285455e-7,0.0007839695,0.0001327643,0.9889995,0.0004057964,0.005502159,0.0002072576],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960683,0.00002128881,0.0004026236,0.001774176,0.00006366274,0.001098605,0.00001430341,0.00003724321,0.0005197907],"genre_scores_gemma":[0.9954847,0.00004927776,0.0006190638,0.0003424578,0.00001016893,0.000183067,0.00007901929,0.00002522044,0.003207047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004034555,"threshold_uncertainty_score":0.5450779,"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."}}