Seasonal flexibility in energy stores of Hudson Bay beluga whales: insights from blubber lipid analysis
Bibliographic record
Abstract
During the primary feeding season, marine mammals often accumulate fat reserves, primarily in the form of blubber. Despite the ecological significance of feeding, uncertainty remains surrounding the timing of energy store accumulation in Hudson Bay beluga whales (Delphinapterus leucas (Pallas, 1776)). Blubber samples were collected from whales hunted by Inuit along the whale’s migration route, from 2015 to 2021 (excl. 2018). Sampling occurred in spring and fall, assumed to represent feeding in winter and summer, respectively. We analyzed blubber for lipid content and adipocyte size, two related indices of lipid dynamics, across three blubber sections (outer, middle, and inner). We found interannual variability in the season with the highest fat content, with some years showing higher lipid content in spring than fall. While adipocyte size did not differ seasonally, minima were observed in 2017 and 2019. Fat stores differed across blubber sections, with the highest lipid content and largest adipocytes in the middle section. The observed seasonal variation indicates there is no consistent season in which Hudson Bay beluga whales predominantly accumulate fat. Consequently, building energy stores in the form of blubber may not be the primary driving force behind the beluga whale’s migration between the wintering and summering areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".