Retrospective analysis of antibiotic treatments against piscirickettsiosis in farmed Atlantic salmon Salmo salar in Chile
Bibliographic record
Abstract
Piscirickettsiosis is the most prevalent salt-water infectious disease in farmed salmonids in Chile. Antimicrobials are used to treat this disease; however, there is growing concern about the poor response to therapeutants on some fish farms. The objective of this study was to assess whether factors such as type of antibiotic used, average fish weight, temperature at the beginning of the treatment, and mortality at the time of treatment administration affect the probability of treatment failure against piscirickettsiosis. Pen-level treatment and production information for the first treatment event from 2014 pens on 118 farms was used in a logistic mixed model to assess treatment failure. We defined a failed treatment as when the average mortality 3 wk after the treatment was above 0.1%. Farm and company were included in the model as random effects. We found that the antibiotic product, mortality level before the treatment, and fish weight at the start of the treatment all had a significant effect on treatment outcome. Our results suggest that antibiotic treatment success is higher if the treatment is administered when mortality associated with piscirickettsiosis is relatively low. We discuss the effect of weight on treatment success and its potential relationships with husbandry practices and drug pharmacokinetics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".