New frontiers in the measurement of energy metabolism
Bibliographic record
Abstract
This perspective highlights three key areas of current and future energy metabolism research: intergenerational health, climate change, and interplanetary exploration. We describe the recent advances in determining estimated energy requirements for a large subset of the general population using the gold standard method for free-living total daily energy expenditure estimates, the doubly labeled water method. The global rise in overweight and obesity demands particular attention to energy requirements in pregnancy and early life, as accumulating evidence contributes to our understanding of intergenerational health transmission and the potential for epigenetic programming in utero. We also acknowledge some gaps in necessary guidelines and understandings of energy requirements for underrepresented populations (i.e., individuals from low and middle-income countries) or those who undergo major physiological changes in new environment (e.g., astronauts). The rising prevalence of excess weight gain, together with climate change, cumulate into a global syndemic exposing vulnerable populations to both malnutrition and the effects of unpredictable and severe weather events, emphasizing the need for varied energetic data accounting for rapid physiological and socioeconomic changes. Finally, we relate how specific estimated energy requirements are needed to account for the energetic challenges specific to extended space travel and ensure the success of interplanetary exploration.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.012 | 0.032 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".