Bibliographic record
Abstract
The wood frog, Rana sylvatica, is the best-studied of a small group of amphibian species that survive whole body freezing during the winter months. These frogs endure the freezing of 65–70% of their total body water in extracellular ice masses. They have implemented multiple adaptations that manage ice formation, deal with freeze-induced ischemia/reperfusion stress, limit cell volume reduction with the production of small molecule cryoprotectants (glucose, urea) and adjust a wide variety of metabolic pathways for prolonged life in a frozen state. All organs, tissues, cells and intracellular organelles are affected by freeze/thaw and its consequences. This article explores mitochondria in the frozen frog with a focus on both the consequences of freezing (e.g., anoxia/ischemia, cell volume reduction) and mitigating defenses (e.g., antioxidants, chaperone proteins, upregulation of mitochondria-encoded genes, enzyme regulation, etc.) in order to identify adaptive strategies that defend and adapt mitochondria in animals that can be frozen for six months or more every year. A particular focus is placed on freeze-responsive genes in wood frogs that are encoded on the mitochondrial genome including ATP6/8, ND4 and 16S RNA. These were strongly up-regulated during whole body freezing (24 h at −2.5 °C) in the liver and brain but showed opposing responses to two component stresses: strong upregulation in response to anoxia but no response to dehydration stress. This indicates that freeze-responsive upregulation of mitochondria-encoded genes is triggered by declining oxygen and likely has an adaptive function in supporting cellular energetics under indeterminate lengths of whole body freezing.
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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) | 0.001 | 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) | 0.001 | 0.001 |
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".