Life history differences parallel environmental differences among North American lake trout (Salvelinus namaycush) populations
Bibliographic record
Abstract
Lake trout ( Salvelinus namaycush ) exhibit substantial life history variation range-wide and at a local scale. This study addresses two hypotheses that have been proposed to account for this: (i) over the zoogeographic range, climatic conditions are associated with life history differences; and (ii) within smaller geographic regions, physical lake attributes are associated with life history differences. Multivariate statistics (Procrustean analysis and canonical correlation analysis) identified a strong, range-wide association between climate and life history variables. Colder climates were associated with slower prematuration growth, older age at maturity, and increased longevity. Winter conditions were also important; longer, warmer winters were associated with slower prematuration growth, smaller maximum sizes, and increased weight at a standard length of 425 mm. In southern populations, these general trends were further modified by physical lake attributes. High productivity lakes had lake trout with faster prematuration growth and larger maximum sizes; deeper lakes were associated with larger maximum sizes and later ages at maturity; and larger lakes were associated with slower prematuration growth, greater longevity, and larger maximum sizes. This study identifies abiotic variables that should be incorporated into existing lake trout management models, thus extending their applicability range-wide and permitting them to deal with possible impacts of climate change.
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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".