Bibliographic record
Abstract
In Focus: Freshwater, C., Trudel, M., Beacham, T. D., Gauthier, S., Johnson, S. C., Neville, C. & Juanes, F. (2016) Individual variation, population-specific migration behaviours and stochastic processes shape marine migration phenologies. Journal of Animal Ecology, 88, 67-78. https://doi.org/10.1111/1365-2656.12852 Pacific salmon undertake arduous and risky migrations from their freshwater nursery grounds to the coastal ocean, northwards to their feeding grounds, and then back to their freshwater natal habitats to spawn. Understanding the phenology of such migrations has largely been viewed through the lens of microevolution producing optimal strategies that reflect local selection pressures; less emphasis has been placed on quantifying how variation in migration patterns can spread the risks associated with life in variable and unpredictable ecosystems. In this issue, Freshwater et al. use the information contained in ear stones (otoliths) and DNA of migrating juvenile sockeye salmon from the Fraser River of western Canada to quantify variation in the timing of their marine migrations. Not only were there population-specific differences in migration phenology of fish from the same river, but there was substantial variation among individuals from specific populations. These patterns also varied from year to year. Data like these emphasize the risks involved in such migrations and suggest that variation in key migration traits are maintained because of the inherent unpredictability of ecosystems. Management and conservation efforts would be well-served to consider actions that maintain such ecological variation to facilitate meta-population persistence in a rapidly changing world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".