Assessing resource use as a predictor for Atlantic salmon ( <i>Salmo salar</i> ) smolt body size
Bibliographic record
Abstract
Abstract Atlantic salmon ( Salmo salar ) have experienced significant population declines in eastern Canada for the past 30 years, primarily attributed to at‐sea mortality. To identify factors contributing to smolt body size, which has been associated with variations in survival, we examined various predictors in linear mixed‐effect models using data from out‐migrating smolts collected from 2000 to 2016 at three rivers: Conne River (NFLD), de la Trinité River (QC) and St. Jean River (QC). Our analysis revealed that resource use (i.e., δ 15 N and δ 13 C) was a significant predictor for smolt length but not condition, explaining 66.2% of the variation in fork length and 19.1% of variation in condition factor. Furthermore, we observed no significant increasing or decreasing trends in body size across the 17‐year period [Correction added on 4 September 2025, after first online publication: The time period in the preceding text has been corrected in this version.] for any rivers studied. Notably, rivers located in the boreal ecoregion did have the largest smolt sizes. This study identifies key predictors of increased smolt body size using a comprehensive long‐term dataset, providing valuable insights into the dynamics of three major salmon rivers in eastern Canada, and indicates stable trends in smolt body size over time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".