Predicting the abundance of <i>Lepeophtheirus salmonis</i> in the Bay of Fundy, New Brunswick
Bibliographic record
Abstract
Abstract Objective The primary objective was to construct a time series model for the abundance of the adult female (AF) sea lice Lepeophtheirus salmonis in Atlantic Salmon Salmo salar farms in the Bay of Fundy, New Brunswick, Canada, for the period 2016–2021 and to illustrate its short-term predictive capabilities. Methods Sea lice are routinely counted for monitoring purposes, and these data are recorded in the Fish-iTrends database. A multivariable autoregressive linear mixed-effects model (second-order autoregressive structure) was generated with the outcome of the abundance of AF sea lice and included treatments, infestation pressures (a measure that represents the dose of exposure of sea louse parasitic stages to potential fish hosts) within sites (internal) and among sites (external), and other predictors. The treatments were categorized by duration and type. Result The effect of mechanical treatments decreased with increasing sea surface temperature. In-sample predictions had good accuracy. A one-standard-deviation increase in the external infestation pressures (EIPAF) produced a significant relative increase in the abundance of AF sea lice by 5% when other model predictors were kept constant. Sites separated by short seaway distances had stronger EIPAF than sites with more considerable distances. Conclusion This model may be helpful for managers and farmers in implementing sea lice mitigation strategies on salmon farms in the Bay of Fundy.
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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 | 0.001 | 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".