The influence of climate change on growth of Arctic charr (Salvelinus alpinus)
Bibliographic record
Abstract
(Linnaeus 1758), are likely to experience changes to growth as a result. Anadromous Arctic charr (charr, hereafter) are of great importance for northern communities, providing a source of income from commercial fisheries and food security from subsistence harvest. Initially, warming is expected to increase the growth of charr, benefitting subsistence and commercial fisheries in the short term. However, over longer time scales, temperatures exceeding the optimum for growth will likely result in metabolic stress, slowed growth, and higher mortality. Thus, the long-term consequences of climate change will likely be negative. We assessed anadromous charr growth from 1984 to 2013 in three stocks around Cumberland Sound using otolith measurements as proxies for age-specific growth. Trend analyses indicated growth had increased in pre-migratory ages over the years. We used mixed models to investigate changes to growth for ages 1-10 in relation to climate variables, finding that growing degree days had the greatest positive influence on ages 1-6 while annual precipitation had an overall negative effect on growth in ages 1-2 and 6-10. Contrary to previous assessments on these stocks, our results suggest charr have indeed experienced changes to growth with climate change. These findings emphasize the need for more thorough long-term growth studies in the management of fisheries as altered growth will affect food security and the economy across the Canadian Arctic.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".