Case definition for clinical and subclinical bacterial kidney disease (BKD) in Atlantic Salmon (<i>Salmo salar</i>L.) in New Brunswick, Canada
Bibliographic record
Abstract
Bacterial kidney disease (BKD) is considered an important cause of loss in salmon aquaculture in Atlantic Canada. Causative agent of BKD is the Gram-positive bacteria Renibacterium salmoninarum. Infected salmon are often asymptomatic (subclinical infection), and the disease is considered chronic. One of the challenges in quantifying information from farm production and health records is the application of a standardized case definition. Case definitions for farm-level and cage-level clinical and subclinical BKD were developed using retrospective longitudinal data from aquaculture practices in New Brunswick, Canada, combining (i) industry records of weekly production data including mortalities, (ii) field observations for BKD using reports of veterinarians and/or fish health technicians, (iii) diagnostic submissions and test results and (iv) treatments used to control BKD. Case definitions were evaluated using veterinarians' expert judgements as reference standard. Eighty-nine and 66% of sites and fish groups, respectively, were associated with BKD at least once. For BKD present (subclinical or clinical), sensitivity and specificity of the case definition were 75-100% varying between event, fish group, site cycle and level (site pen). For clinical BKD, sensitivities were 29-64% and specificities 91-100%. Industry data can be used to develop sensitive case definitions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".