Humans in the cold: Regulating energy balance
Bibliographic record
Abstract
For humans to maintain a stable core temperature in cold environments, an increase in energy expenditure (EE) is required. However, little is known about how cold stimulus impacts energy balance as a whole, as energy intake (EI) has been largely overlooked. This review focuses on the current state of knowledge regarding how cold exposure (CE) impacts both EE and EI, while highlighting key gaps and shortcomings in the literature. Animal models clearly reveal that CE produces large increases in EE, while decreasing environmental temperatures results in a significant negative dose-response effect in EI (r=-.787, P<.001), meaning animals eat more as temperature decreases. In humans, multiple methods are used to administer cold stimuli, which result in consistent yet quantitatively small increases in EE. However, only two studies have measured ad libitum food intake in combination with acute CE in humans. Chronic CE (i.e., cold acclimation) studies have been shown to produce minimal changes in body weight, with an average compensation of ~126%. Although more studies are required to investigate how cold impacts EI in humans, results presented in this review warrant caution before presenting or considering CE as a potential adjunct to weight loss strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".