Compensation in response to energy deficits induced by exercise or diet
Bibliographic record
Abstract
Obesity is an extremely resilient condition. Weight loss is most challenging, and weight recidivism is rampant. There is accumulating evidence highlighting that energy deficits meant to produce increased mobilization of energy stores trigger a number of somewhat persistent adaptations that together increase the drive to eat and decrease energy output. These adaptations ostensibly enable a context where the likelihood of energy compensation is heightened. In fact, energy compensation is present for both diet and exercise induced energy deficits although at different magnitudes. For the most part, the energy compensation in response to exercise induced energy deficits seems to be larger. Interestingly, energy compensation appears to be greater for longer interventions, an effect independent of whether the energy deficit is induced through diet or exercise. The latter suggests that the increased drive to eat and the reduced energy expenditure that accompany weight loss might be successfully fought off initially. However, with time there seems to be increasing erosion of the behaviours that initially opposed adaptations to weight loss and increased energy compensation progressively sets in. Under such conditions, it would seem prudent to propose weight loss targets that align with a level of behaviour modifications that can be sustained indefinitely.
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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 | 0.002 | 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 | 0.001 |
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".