Cortisol levels in beluga whales (Delphinapterus leucas): Setting a benchmark for Marine Protected Area monitoring
Bibliographic record
Abstract
Beluga whales (Delphinapterus leucas) are facing profound changes in their habitat, with impacts expected at the individual and population level. Detecting and monitoring exposure and response to environmental stressors is necessary for beluga conservation and management of human activities. Cortisol has proven as a useful tool to assess stress on wildlife. Cortisol was measured in three blubber layers and plasma in subsistence-hunted beluga whales from the summers of 2007 to 2010 using an HPLC/MS/MS. We assessed the effect of biological and biochemical factors. Cortisol ranged from undetectable to 17.8 ng/g in blubber and 2.5 to 61.2 ng/mL in plasma. Concentrations were highest in the inner blubber layer likely reflecting circulating levels. All tissues were significantly higher in 2008 for reasons that remain unclear. Cortisol levels were on par with resting levels in captive belugas. Best fit models for cortisol revealed age to be an important determinant along with length and blubber thickness. Lack of relationships with biochemical factors such as organic contaminants suggests current cortisol levels are not significantly influenced by present contaminant concentrations. Our findings support the use of middle and outer blubber tissues for an integrated measure of chronic stress that are less subject to the influence of acute stress.
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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".