Mixtures effect of antibiotics on soil nitrification and denitrification - an experimental and modelling approach
Bibliographic record
Abstract
Impacts of antibiotic mixtures on microbial ecosystem functions need to be unraveled to\nimprove the environmental risk assessment. This study aims to characterize and predict the\ntoxicity of antibiotic mixtures on soil nitrification and denitrification processes.\nDifferent antibiotics (tetracycline, sulfonamide, macrolide, fluoroquinolone) with distinct\nmechanisms of action, were tested alone and in mixtures by dose-response approaches on\nsoil substrate-induced nitrification (SIN) and minimum inhibitory concentration of soil den-\nitrifier enrichment (MIC-E). Abundance of ammonia-oxidizers (archeal and bacterial) and\ndenitrifiers (clade I and II of N2O-reducers) were investigated by molecular approach. For\nnitrification, the experimental results for different antibiotic mixture ratio were further ana-\nlyzed regarding mathematical modeling interpretation based on the concepts of concentration\naddition (CA) or independent action (IA).\nThe SIN bioassay highlighted bacteriostatic effects at sulfonamide or macrolides concen-\ntrations lower than observed environmental concentrations. The differences in toxicities of\nantibiotics individually tested were explained by the sorption coefficient of the various an-\ntibiotics. Surprisingly, despite different cellular targets of the tested antibiotics, modelling\nmixture analyses revealed few differences in toxicity prediction between IA or CA concepts.\nDenitrification showed a higher tolerance than nitrification to the antibiotics and the lowest\nMIC-E for tetracycline. For nitrification and denitrification, the mixture toxicity was mainly\ndriven by the more toxic compound.\nThe archeal/bacterial nitrifiers ratio was positively related to the magnitude of nitrification\ninhibition; archaea showing invariant abundances while the antibiotic doses increased. The\nabundances of the denitrifier nosZ clade II greatly decreased under any antibiotic exposures\nunlike the nosZ clade I guilds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".