Effect of timing of count events on estimates of sea lice abundance and interpretation of effectiveness following bath treatments
Bibliographic record
Abstract
Effectiveness of sea lice bath treatment is often assessed by comparing pre- and post-treatment counts. However, in practice, the post-treatment counting window varies from the day of treatment to several days after treatment. In this study, we assess the effect of post-treatment lag time on sea lice abundance estimates after chemical bath treatment using data from the sea lice data management program (Fish-iTrends) between 2010 and 2014. Data on two life stages, (i) adult female (AF) and (ii) pre-adult and adult male (PAAM), were aggregated at the cage level and log-transformed. Average sea lice counts by post-treatment lag time were computed for AF and PAAM and compared relative to treatment day, using linear mixed models. There were 720 observations (treatment events) that uniquely matched pre- and post-treatment counts from 53 farms. Lag time had a significant effect on the estimated sea lice abundance, which was influenced by season and pre-treatment sea lice levels. During summer, sea lice were at a minimum when counted 1 day post-treatment irrespective of pre-treatment sea lice levels, whereas in the spring and autumn, low levels were observed for PAAM over a longer interval of time, provided the pre-treatment sea lice levels were >5-10.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".