Counting sea lice on Atlantic salmon farms : empirical and theoretical observations
Bibliographic record
Abstract
This communication briefly reviews some of the factors which have shaped the current protocols for lice counting on salmon farms and points out that the motivation for counting is not always the same. It is also apparent that a number of widely accepted assumptions, such as those relating to presumed lice population distributions or the ability to pre-select highly infested cages, cannot be uncritically accepted. Recent research from Scotland, Norway and Canada has demonstrated that the fish on farm sites are clustered in cages which have significant differences in lice abundance. Moreover, the prevalence and distribution of lice in farmed and wild fish populations have distinct patterns. At low prevalence the distributions can be described by the negative binomial distribution, whereas at high prevalence lice tend to be normally distributed. The monitoring strategy of sampling the most infested cage on a farm for early detection of a breach of treatment trigger levels for lice is flawed. These findings need to be taken into account when sampling protocols for lice are designed. In particular, precision in estimating prevalence and abundance of lice on the site requires random sampling from many cages. There is no evidence of systematic bias rising from the use of farm staff counting sea lice compared with dedicated counting teams.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".