Bibliographic record
Abstract
Winter survival for many kinds of animals involves freeze tolerance, the ability to endure the conversion of about 65% of total body water into extracellular ice and the consequences that freezing imposes including interruption of vital processes (e.g., heartbeat and breathing), cell shrinkage, elevated osmolality, anoxia/ischemia, and potential physical damage from ice. Freeze-tolerant animals include various terrestrially hibernating amphibians and reptiles, many species of insects, and numerous other invertebrates inhabiting both terrestrial and intertidal environments. Well-known strategies of freezing survival include accumulation of low molecular mass carbohydrate cryoprotectants (e.g., glycerol), use of ice nucleating agents/proteins for controlled triggering of ice growth and of antifreeze proteins that inhibit ice recrystallization, and good tolerance of anoxia and dehydration. The present article focuses on more recent advances in our knowledge of the genes and proteins that support freeze tolerance and the metabolic regulatory mechanisms involved. Important roles have been identified for aquaporins and transmembrane channels that move cryoprotectants, heat shock proteins and other chaperones, antioxidant defenses, and metabolic rate depression. Genome and proteome screening has revealed many new potential targets that respond to freezing, in particular implicating cytoskeleton remodeling as a necessary facet of low temperature and/or cell volume adaptation. Key regulatory mechanisms include reversible phosphorylation control of metabolic enzymes and microRNA control of gene transcript expression. These help to remodel metabolism to preserve core functions while suppressing energy expensive metabolic activities such as the cell cycle. All of these advances are providing a much more complete picture of life in the frozen state.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".