Bibliographic record
Abstract
Many environmental conditions can constrain the ability of animals to obtain sufficient food energy, or transform that food energy into useful chemical forms. To survive extended periods under such conditions animals must suppress metabolic rate to conserve energy, water, or oxygen. Amongst small endotherms, this metabolic suppression is accompanied by and, in some cases, facilitated by a decrease in core body temperature-hibernation or daily torpor-though significant metabolic suppression can be achieved even with only modest cooling. Within some ectotherms, winter metabolic suppression exceeds the passive effects of cooling. During dry seasons, estivating ectotherms can reduce metabolism without changes in body temperature, conserving energy reserves, and reducing gas exchange and its inevitable loss of water vapor. This overview explores the similarities and differences of metabolic suppression among these states within adult animals (excluding developmental diapause), and integrates levels of organization from the whole animal to the genome, where possible. Several similarities among these states are highlighted, including patterns and regulation of metabolic balance, fuel use, and mitochondrial metabolism. Differences among models are also apparent, particularly in whether the metabolic suppression is intrinsic to the tissue or depends on the whole-animal response. While in these hypometabolic states, tissues from many animals are tolerant of hypoxia/anoxia, ischemia/reperfusion, and disuse. These natural models may, therefore, serve as valuable and instructive models for biomedical research.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".