Bibliographic record
Abstract
Animals must regulate the fluxes of multiple fuels to support changing metabolic rates that result from variation in physiological circumstances. The aim of fuel selection strategies is to exploit the advantages of individual substrates while minimizing the impact of disadvantages. All exercising mammals share a general pattern of fuel selection: at the same %V(O(2,max)) they oxidize the same ratio of lipids to carbohydrates. However, highly aerobic species rely more on intramuscular fuels because energy supply from the circulation is constrained by trans-sarcolemmal transfer. Fuel selection is performed by recruiting different muscles, different fibers within the same muscles or different pathways within the same fibers. Electromyographic analyses show that shivering humans can modulate carbohydrate oxidation either through the selective recruitment of type II fibers within the same muscles or by regulating pathway recruitment within type I fibers. The selection patterns of shivering and exercise are different: at the same %V(O(2,max)), a muscle producing only heat (shivering) or significant movement (exercise) strikes a different balance between lipid and carbohydrate oxidation. Long-distance migrants provide an excellent model to characterize how to increase maximal substrate fluxes. High lipid fluxes are achieved through the coordinated upregulation of mobilization, transport and oxidation by activating enzymes, lipid-solubilizing proteins and membrane transporters. These endurance athletes support record lipolytic rates in adipocytes, use lipoprotein shuttles to accelerate transport and show increased capacity for lipid oxidation in muscle mitochondria. Some migrant birds use dietary omega-3 fatty acids as performance-enhancing agents to boost their ability to process lipids. These dietary fatty acids become incorporated in membrane phospholipids and bind to peroxisome proliferator-activated receptors to activate membrane proteins and modify gene expression.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".