The energetics of childhood: Current knowledge and insights into human variation, evolution, and health
Bibliographic record
Abstract
How organisms capture and ultimately use metabolic energy-a limiting resource of life-has profound implications for understanding evolutionary legacies and current patterns of phenotypic variation, adaptation, and health. Energetics research among humans has a rich history in biological anthropology and beyond. The energetics of childhood, however, remains relatively underexplored. This shortcoming is notable given the accepted importance of childhood in the evolution of the unique human life history pattern as well as the known sensitivity of childhood development to local environments and lived experiences. In this review, I have three objectives: (1) To overview current knowledge regarding how children acquire and use energy, highlighting work among diverse human populations and pointing to recent advances and remaining areas of uncertainty; (2) To discuss key applications of this knowledge for understanding human variation, evolution, and health; (3) To recommend future avenues for research. A growing body of evidence supports a model of trade-offs and constraint in childhood energy expenditure. This model, combined with advancements on topics such as the energetics of immune activity, the brain, and the gut, provides insights into the evolution of extended human subadulthood and the nature of variation in childhood development, lifetime phenotype, and health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".