Variation in pre‐treatment count lead time and its effect on baseline estimates of cage‐level sea lice abundance
Bibliographic record
Abstract
Treatment efficacy studies typically use pre-treatment sea lice abundance as the baseline. However, the pre-treatment counting window often varies from the day of treatment to several days before treatment. We assessed the effect of lead time on baseline estimates, using historical data (2010-14) from a sea lice data management programme (Fish-iTrends). Data were aggregated at the cage level for three life stages: (i) chalimus, (ii) pre-adult and adult male and (iii) adult female. Sea lice counts were log-transformed, and mean counts by lead time relative to treatment day were computed and compared separately for each life stage, using linear mixed models. There were 1,658 observations (treatment events) from 56 sites in 5 Bay Management Areas. Our study showed that lead time had a significant effect on the estimated sea lice abundance, which was moderated by season. During the late summer and autumn periods, counting on the day of treatment gave significantly higher values than other days and would be a more appropriate baseline estimate, while during spring and early summer abundance estimates were comparable among counts within 5 days of treatment. A season-based lead time window may be most appropriate when estimating baseline sea lice levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".