Long-term antibiotic exposure in soil is associated with changes in microbial community structure and prevalence of class 1 integrons
Bibliographic record
Abstract
Antimicrobial resistance is one of the most significant challenges facing the global medical community and can be attributed to the use and misuse of antibiotics. This includes use as growth promoters or for prophylaxis and treatment of bacterial infection in intensively farmed livestock from where antibiotics can enter the environment as residues in manure. We characterised the impact of the long-term application of a mixture of veterinary antibiotics alone (tylosin, sulfamethazine and chlortetracycline) on class 1 integron prevalence and soil microbiota composition. Class 1 integron prevalence increased significantly (P < 0.005) from 0.006% in control samples to 0.064% in the treated plots. Soil microbiota was analysed using 16S rRNA gene sequencing and revealed significant alterations in composition. Of the 19 significantly different (P < 0.05) OTUs identified, 16 were of the Class Proteobacteria and these decreased in abundance relative to the control plots. Only one OTU, of the Class Cyanobacteria, was shown to increase in abundance significantly; a curiosity given the established sensitivity of this class to antibiotics. We hypothesise that the overrepresentation of Proteobacteria as OTUs that decreased significantly in relative abundance, coupled with the observations of an increase in integron prevalence, may represent a strong selective pressure on these taxa.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".