A prospective matched nested case–control study of bacterial gill disease outbreaks in Ontario, Canada government salmonid hatcheries
Bibliographic record
Abstract
Early-rearing salmonids in Ontario, Canada government fish hatcheries have been persistently affected by bacterial gill disease (BGD), and outbreaks at these locations have often been associated with high morbidity and mortality. The causative agent of BGD, Flavobacterium branchiophilum, is ubiquitous in fresh water, and outbreaks of BGD are considered to be associated with deleterious environmental conditions. This paper summarizes a 14-month rearing unit-level prospective nested matched case-control investigation at six Ontario government hatcheries (raising a total of six different salmonid species) to identify, and quantify the effects of, important predictors of BGD outbreaks. Ongoing husbandry data were collected on all early-rearing (<9 months of age) fish tank-lots ("tank-lot"=a group of fish from a specific lot existing in a single hatchery tank for a given period during the study time frame) at participating hatcheries, and all outbreaks of BGD were confirmed by light microscopy during the study period. Control tank-lots were selected at the end of the study and matched to individual cases based on time, hatchery, and species. Data were analyzed using logistic regression modeling, controlling for fish age. The final multivariable model indicated that affected tank-lots were significantly more likely to have had lower fish numbers, lower individual fish weights, higher mortality levels and higher feeding rates during the week preceding observed BGD outbreaks than were asymptomatic control tank-lots. Refinements in the observation and manipulation of these factors could therefore aid in the prevention of fish losses associated with observable BGD outbreaks. The predictive (as opposed to causal) nature of the identified factors needs to be considered, and further research is required to understand the relationships between these factors and BGD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".