Influence of sex, migration distance, and latitude on life history expression in steelhead and rainbow trout (<i>Oncorhynchus mykiss</i>)
Bibliographic record
Abstract
In partially migratory species, such as Oncorhynchus mykiss, the emergence of life history phenotypes is often attributed to fitness trade-offs associated with growth and survival. Fitness trade-offs can be linked to reproductive tactics that vary between the sexes, as well as the influence of environmental conditions. We found that O. mykiss outmigrants are more likely to be female in nine populations throughout western North America (grand mean 65% female), in support of the hypothesis that anadromy is more likely to benefit females. This bias was not related to migration distance or freshwater productivity, as indicated by latitude. Within one O. mykiss population we also measured the resident sex ratio and did not observe a male bias, despite a high female bias among outmigrants in that system. We provide a simulation to demonstrate the relationship between sex ratios and the proportion of anadromy and show how sex ratios could be a valuable tool for predicting the prevalence of life history types in a population.
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 | 0.000 |
| 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".