Photoperiodic Remodeling of Adiposity and Energy Metabolism in Non-Human Mammals
Bibliographic record
Abstract
Energy homeostasis and metabolism in mammals are strongly influenced by seasonal changes. Variations in photoperiod patterns drive adaptations in body weight and adiposity, reflecting changes in the regulation of food intake and energy expenditure. Humans also show distinct patterns of energy balance depending on the season, being more susceptible to gaining weight during a specific time of the year. Changes in body weight are mainly reflected by the adipose tissue, which is a key metabolic tissue and is highly affected by circannual rhythms. Mostly, in summer-like (long-active) photoperiod, adipocytes adopt a rather anabolic profile, more predisposed to store energy, while food intake increases and energy expenditure is reduced. These metabolic adaptations involve molecular modifications, some of which have been studied during the last years and are summarized in this review. In addition, there is a bidirectional relation between obesity and the seasonal responses, with obesity disrupting some of the seasonal responses observed in healthy mammals, and altered seasonality being highly associated with increased risk of developing obesity. This suggests that changes in photoperiod produce important metabolic alterations in healthy organisms. Biological rhythms impact the regulation of metabolism to different extents, some of which are already known, but further research is needed to fully understand the relationship between energy balance and seasonality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".