Patterns of energy allocation during energetic scarcity; evolutionary insights from ultra-endurance events
Bibliographic record
Abstract
Exercise physiologists and evolutionary biologists share a research interest in determining patterns of energy allocation during times of acute or chronic energetic scarcity. Within sport and exercise science, this information has important implications for athlete health and performance. For evolutionary biologists, this would shed new light on our adaptive capabilities as a phenotypically plastic species. In recent years, evolutionary biologists have begun recruiting athletes as study participants and using contemporary sports as a model for studying evolution. This approach, known as human athletic palaeobiology, has identified ultra-endurance events as a valuable experimental model to investigate patterns of energy allocation during conditions of elevated energy demand, which are generally accompanied by an energy deficit. This energetic stress provokes detectable functional trade-offs in energy allocation between physiological processes. Early results from this modelsuggest thatlimited resources are preferentially allocated to processes which could be considered to confer the greatest immediate survival advantage (including immune and cognitive function). This aligns with evolutionary perspectives regarding energetic trade-offs during periods of acute and chronic energetic scarcity. Here, we discuss energy allocation patterns during periods of energetic stress as an area of shared interest between exercise physiology and evolutionary biology. We propose that, by addressing the ultimate "why" questions, namely why certain traits were selected for during the human evolutionary journey, an evolutionary perspective can complement the exercise physiology literature and provide a deeper insight of the reasons underpinning the body's physiological response to conditions of energetic stress.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".