Methodological influence on detecting temperature effects on growth variability in juvenile Atlantic salmon
Bibliographic record
Abstract
Abstract In the context of warming freshwater habitats, protection of Atlantic salmon populations requires an understanding of the effects of temperature on somatic growth during the juvenile life stage. However, quantifying the effect of temperature on growth is challenging given differences among methodologies, metrics of growth, and their underlying assumptions. Using short term studies (2000–2002) in two Canadian populations of wild Atlantic salmon (Margaree and Miramichi rivers), we investigate whether different hierarchical modeling approaches influence the derivation of temperature-growth relationships, by contrasting seasonal growth trajectories (von Bertalanffy; VBGF) to size-at-age data models built with instantaneous growth rates. Size-at-age data analysed seasonally with the VBGF framework failed to detect an effect of temperature, whereas instantaneous growth rates from the same dataset were strongly related to temperature metrics. However, instantaneous growth rates cannot be used to extrapolate predictions into meaningful metrics for fisheries management (e.g., size at the end of the growing season). Nevertheless, we show that size at the end of the growing season can be predicted with VBGF models accounting for site-level variation, which in turn are related to temperature metrics, as observed for instantaneous growth rates. Taken together, these results show that combining these two approaches (size-at-age, growth rates) can circumvent their intrinsic drawbacks and reveal essential ecological patterns that may otherwise remain undetected. In cases where instantaneous growth rates are not available, relating predicted size-at-age from hierarchical VBGF to temperature provides an interesting alternative for detecting subtle environmental effects, even if the VBGF parameters or its residuals are unrelated to temperature metrics.
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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 | 0.022 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".