No rest for the rodent: energy management strategies in the naked mole-rat
Bibliographic record
Abstract
Abstract Organisms have access to a limited amount of energy that must be distributed among multiple physiological processes. Broadly, the total daily energy expenditure (DEE) can be partitioned into maintenance costs (i.e., resting metabolic rate RMR) and active energy expenditure (AEE). The slope ( b ) between DEE and RMR provides insights into energy management strategies. In the additive model, where changes in activity are independent of maintenance energy, DEE and RMR follow a part-whole relationship with b =1. In the allocation model, where increased activity requires compensatory reductions in maintenance costs, a limit on DEE causes a DEE-RMR relationship with b <1. In the performance model, increased activity causes an increase in maintenance costs, which causes a DEE-RMR relationship with b >1. Despite their high lifetime energy expenditure and resistance to age-related metabolic decline, energy management is yet to be explored in the African naked mole-rat (NMR, Heterocephalus glaber ). To investigate metabolic strategies in the NMR, repeated metabolic and activity measurements were taken in 32 individual NMRs using a multiplexed metabolic system. DEE was not repeatable, thus the DEE-RMR covariance at the among-individual level could not be fitted. At the within-individual level, however, the positive correlation between RMR and activity and the DEE-RMR relationship with b > 1 indicated support for the performance model. Hence, our results indicate that within-individual changes in activity and RMR are associated, suggesting that when a NMR increases activity on a given day, the impact on DEE are disproportionate because of a concurrent increase RMR.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".