Body condition impacts blood and muscle oxygen storage capacity of free-living beluga whales (<i>Delphinapterus leucas</i>)
Bibliographic record
Abstract
Arctic marine ecosystems are currently undergoing rapid environmental changes. Over the past 20 years, individual growth rates of beluga whales (Delphinapterus leucas) have declined, which may be a response to climate change; however, scarcity of physiological data makes it difficult to gauge the adaptive capacity and resilience of the species. We explored relationships between body condition and physiological parameters pertaining to oxygen (O2) storage capacity in 77 beluga whales in the eastern Beaufort Sea. Muscle myoglobin concentrations averaged 77.9 mg g−1, one of the highest values reported among mammals. Importantly, blood hematocrit, hemoglobin, and muscle myoglobin concentrations correlated positively to indices of body condition, including maximum half-girth to length ratios. Thus, a whale with the lowest body condition index would have ∼27% lower blood (26.0 vs. 35.7 mL kg−1) and 12% lower muscle (15.6 vs. 17.7 mL kg−1) O2 stores than a whale of equivalent mass with the highest body condition index; with the conservative assumption that underwater O2 consumption rates are unaffected by body condition, this equates to a >3 minute difference in maximal aerobic dive time between the two extremes (14.3 vs. 17.4 minutes). Consequently, environmental changes that negatively impact body condition may hinder the ability of whales to reach preferred prey sources, evade predators, and escape ice entrapments. The relationship between body condition and O2 storage capacity may represent a vicious cycle, in which environmental changes resulting in decreased body condition impair foraging, leading to further reductions in condition through diminished prey acquisition and/or increased foraging efforts.
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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) | 0.001 | 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".