Long-term survival of adult Arctic grayling (<i>Thymallus arcticus</i>) in the Kuparuk River, Alaska
Bibliographic record
Abstract
In many long-lived species such as Arctic grayling (Thymallus arcticus), population growth rate is most sensitive to changes in adult survival probabilities. Understanding the factors that regulate adult survival in this species should provide insight into the population dynamics of this and other long-lived Arctic species. Using the program MARK, we analyzed 17 years of markrecapture data to estimate survival rates for Arctic grayling in the Kuparuk River, Alaska, from 1985 to 2000. Mean annual survival rates overall ranged from 0.39 to 1.0 and averaged 0.71 ± 0.05 for resident and 0.75 ± 0.05 for nonresident fish. Spending the summer in the more productive fertilized zone of the experimental reach had no influence on survival despite higher productivity on all trophic levels and consistently higher growth rates in Arctic grayling. None of the environmental (stream temperature, discharge, winter severity, and incidence of drought) or population parameters (growth, condition factor, and mean fish size) that we examined explained significant amounts of variance in survival rates. The lack of responsiveness of survival to annual environmental conditions was unexpected and suggests that multiyear factors or life history tactics that maintain survival at the expense of growth and fecundity likely determine survival.
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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 | 0.000 |
| 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) | 0.000 | 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".