Rearing unit-level factors associated with bacterial gill disease treatment in two Ontario, Canada government salmonid hatcheries
Bibliographic record
Abstract
Early-rearing salmonids in Ontario Ministry of Natural Resources (OMNR) fish hatcheries have been consistently affected by bacterial gill disease (BGD) (causative agent: Flavobacterium branchiophilum) for many years. Separate retrospective epidemiological investigations of BGD treatments at two OMNR fish hatcheries (Hatcheries A and B) for the 1999 production year were conducted using on-site hatchery records. Both investigations were carried out at the rearing unit-level, with early-rearing (<9 months of age) "tank-lot" as the unit of analysis to identify unique fish populations over time. Multivariable repeated measures logistic regression models were created for both hatchery datasets, controlling for lot-level and species effects. For Hatchery A, the species brook trout (Salvelinus fontinalis) and brown trout (Salmo trutta) were significantly associated with BGD treatment, as well as lower water exchange rate, and higher feeding and mortality percentages during the 2 weeks previous to BGD treatment. At Hatchery B, the species brook trout (S. fontinalis) and splake (Salvelinus namaycush x S. fontinalis) were significantly associated with BGD treatment, as well as lower individual fish weights and treatment for BGD during the previous week. These results emphasize the importance of water quality, feeding rate, fish size and prior mortality on the development of BGD. Significant hatchery and species effects were evident, and future observational research on BGD must account for these factors in their design and analysis.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".