A model for estimating mortality of Atlantic salmon,<i>Salmo salar</i>, between spawning events
Bibliographic record
Abstract
We developed a Bayesian hierarchical model to estimate annual mortality of repeat-spawning Atlantic salmon, Salmo salar, that distinguishes between mortality rates and the confounding effects of consecutive-year and alternate-year repeat-spawning strategies. The model provides annual estimates of two mortality rates: mortality in the first year (Z1), a time period during which salmon are primarily in freshwater (staging, spawning, and overwintering) followed by a brief period at sea, and mortality in the second year (Z2) when salmon are predominantly at sea. When fit to data for the LaHave River (Nova Scotia, Canada) salmon population, Z1 showed an increasing trend throughout the time series, whereas Z2 also increased but in a single, stepwise manner. Once a time series of mortality rates was separated from the other life-history parameters, we were able to demonstrate how they could be used for examining the influence of environmental conditions by comparing the estimated mortality rate time series with the North Atlantic Oscillation Index (NAOI). This comparison uncovered a statistically significant correlation between the NAOI and the survival in the second year after spawning that would not have been evident had the mortality estimation model not been developed.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".