Sea lice monitoring on <scp>A</scp>tlantic salmon farms in <scp>N</scp>ew <scp>B</scp>runswick, <scp>C</scp>anada: comparing audit and farm staff counts
Bibliographic record
Abstract
Sea lice audits were performed by the Atlantic Veterinary College on commercial aquaculture sites in New Brunswick, Canada, in 2011. Although the primary objective was to verify that farms were reporting similar lice counts to third-party counts, more detailed comparisons were made to identify when lice counts were more likely to differ between the audit team and farm employees. A total of 28 sea lice audits were conducted on 16 sites between June and December 2011. During each audit, 10 cages were evaluated per site where possible, with ten fish per cage being evaluated by an audit technician and a further ten by a farm employee. Data analysis included descriptive statistics of lice counts by stage and limits of agreement plots. A random effects negative binomial model that accounted for clustering of cages within sites was applied to assess the effect of counter type and season on lice counts by stage. The results indicate that farms counts were generally in agreement with audit counts. However, when the average counts for chalimus and preadult (male and female) and adult male lice stages were high, farm counters were more likely to report a lower value. Higher lice counts were observed during autumn compared to summer especially for the adult female stage. Finally, there was a significant clustering effect for site and cage, with most of the variation attributable to site.
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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 | 0.003 |
| 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 | 0.000 | 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".