Spatiotemporal variation of ringed seal blubber cortisol levels in the Canadian Arctic
Bibliographic record
Abstract
Abstract Climate change in the Arctic has widespread and complex effects on the health of animals and their populations. We used radioimmunoassay to measure blubber cortisol in ringed seals (Pusa hispida) sampled in Ulukhaktok, Inuvialuit Settlement Region, Northwest Territories, Canada (spring, 2002, 2004–2005, 2007–2012) and Arviat, Nunavut, Canada (autumn, 2003–2012) to examine chronic stress relative to biology (age, sex, length), body condition (blubber depth), and diet (δ13C, δ15N, and isotopic niche size). Ulukhaktok ringed seals had higher cortisol concentrations overall (0.46 ± 0.04 ng/g) than Arviat ringed seals (0.36 ± 0.03 ng/g), and these higher concentrations were associated with higher muscle δ15N and lower blubber thickness. In contrast, blubber cortisol concentrations for Arviat ringed seals decreased with blubber depth and increased with age, though testing of age effects individually suggests that age-related patterns are weak. Annual mean cortisol concentration increased from 2003 to 2012 in Arviat ringed seals, but low sample sizes precluded analysis of annual patterns for Ulukhaktok ringed seals. The trend of increased cortisol over time in Arviat ringed seals suggests that they might be experiencing greater chronic stress over time, which could have implications for numerous population health metrics including reproduction and pup recruitment.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".