Aquaculture changes the profile of antibiotic resistance and mobile genetic element associated genes in Baltic Sea sediments
Bibliographic record
Abstract
Antibiotics are commonly used in aquaculture and they can change the environmental resistome by increasing antibiotic resistance genes (ARGs). Sediment samples were collected from two fish farms located in the Northern Baltic Sea, Finland, and from a site outside the farms (control). The sediment resistome was assessed by using a highly parallel qPCR array containing 295 primer sets to detect ARGs, mobile genetic elements and the 16S rRNA gene. The fish farm resistomes were enriched in transposon and integron associated genes and in ARGs encoding resistance to antibiotics which had been used to treat fish at the farms. Aminoglycoside resistance genes were also enriched in the farm sediments despite the farms not having used aminoglycosides. In contrast, the total relative abundance values of ARGs were higher in the control sediment resistome and they were mainly genes encoding efflux pumps followed by beta-lactam resistance genes, which are found intrinsically in many bacteria. This suggests that there is a natural Baltic sediment resistome. The resistome associated with fish farms can be from native ARGs enriched by antibiotic use at the farms and/or from ARGs and mobile elements that have been introduced by fish farming.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 source (direct Gemma or distilled Codex), 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".