Bibliographic record
Abstract
In the world of physiology, scientists use model organisms, such as fish, rats and mice, to study how the complex relationship between the body and its environment influences behaviour. Yet, few have the courage to tackle the challenges of working with the largest mammals on Earth. Undeterred, Hope Ball from University of Akron, USA, and her collaborators from several US institutions decided to investigate how bowhead whales (Balaena mysticetus) and belugas (Delphinapterus leucas) tackle the physical challenges of survival in the extreme environment of the Arctic Ocean and their lengthy migrations.Whales have a unique layer of tissue under their skin called blubber, which provides buoyancy and thermal insulation in cold waters in addition to allowing them to store large amounts of fat to fuel migration while fasting. Other animals that undergo long periods of fasting also possess a specialized protein hormone, called leptin, that varies seasonally to suppress appetite and stimulate fat breakdown when the animals have stopped feeding. While whales are known to produce leptin, little was known about production of the hormone and how the levels vary in these animals.Ball and her colleagues partnered with residents of several Inupiat settlements during their annual subsistence hunt, to collect blubber and tissue samples from bowhead and beluga juveniles and adults during the autumn migration and in the spring. In samples collected from the whales in the autumn – following an intense summer feeding period – the authors reported that leptin gene expression was approximately 10- to 50-fold higher than in the spring whales, suggesting that extreme seasonal variation in leptin plays a role in regulating fat breakdown during fasting periods. The authors also reported that the largest amount of leptin was found in the deepest layer of blubber, where fat is actively broken down for energy production when food is unavailable. This suggests that leptin levels, in addition to seasonal cues, may drive the whales’ migration to and from summer feeding grounds.To determine the influence of season on fat utilization, the authors analysed the activity of the enzymes that break down fat. They found that in the autumn, when the whales were well fed, the blubber was composed of large fat cells and the activity of these enzymes was low. However, in winter, following migration from cold waters back to summer feeding grounds, the blubber contained smaller fat cells and more connective tissue, and the activity of the enzymes that break down fat was high. This suggests that the whales break down fat to maintain the energy demands of the body when they migrate. In addition, Ball and colleagues reported low levels of leptin in juveniles, which were similar to those found in the spring fasting adults, suggesting that the metabolism of the young whales is low to minimize energy loss and it is directed at stimulating appetite to ensure maturation.It appears that these extreme seasonal and life stage variations in leptin levels are unique adaptations that Arctic whales have evolved to drive fat utilization, feeding behaviour and initiation of migration in cold waters. In whales, leptin controls appetite and fat breakdown, thus making blubber an active player in a whale's physiology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.040 | 0.024 |
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".