Fuel Use in Mammals: Conserved Patterns and Evolved Strategies for Aerobic Locomotion and Thermogenesis
Bibliographic record
Abstract
SYNOPSIS: Effective aerobic locomotion depends on adequate delivery of oxygen and an appropriate allocation of metabolic substrates. The use of metabolic substrates during exercise follows a predictive pattern of lipid and carbohydrate oxidation that is similar in lowland native cursorial mammals. We have found that in two highland lineages of mice (Phyllotis and Peromyscus) the fuel use pattern is shifted to a greater reliance on carbohydrates compared to their lowland conspecifics and congenerics. However, there is variation between lineages in the importance of phenotypic plasticity in the expression of this metabolic phenotype. Moreover, this metabolic phenotype is independent of running aerobic capacity and can also be independent of thermogenic capacity. For example, wild-caught mice from a highland population of deer mice (Peromyscus maniculatus) housed in warm normoxic laboratory conditions maintain higher maximum cold-induced oxygen consumption in acute hypoxia than lowland congenerics, but shivering and non-shivering thermogenesis is supported by high rates of lipid oxidation. This is reflected in the consistently higher activities of oxidative and fatty acid oxidation enzymes in the gastrocnemius of highland deer mice compared to lowlanders, which are resistant to hypoxia acclimation. While a fixed trait in muscle aerobic capacity may reflect the pervasive and unremitting low PO2 at high altitudes, muscle capacities for substrate oxidation may be more flexible to match appropriate substrate use with changing energetic demands. How shivering thermogenesis and locomotion potentially interact in the matching of muscle metabolic capacities to appropriate substrate use is unclear. Perhaps it is possible that shivering serves as "training" to ensure muscles have the capacity to support locomotion or visa-versa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".