Real-time measurement of metabolic rate during freezing and thawing of the wood frog,<i>Rana sylvatica</i>: implications for overwinter energy use
Bibliographic record
Abstract
Ectotherms overwintering in temperate ecosystems must survive low temperatures while conserving energy to fuel post-winter reproduction. Freeze-tolerant wood frogs, Rana sylvatica, have an active response to the initiation of ice formation that includes mobilising glucose from glycogen and circulating it around the body to act as a cryoprotectant. We used flow-through respirometry to measure CO(2) production ( ) in real time during cooling, freezing and thawing. CO(2) production increases sharply at three points during freeze-thaw: at +1°C during cooling prior to ice formation (total of 104±17 μl CO(2) frog(-1) event(-1)), at the initiation of freezing (565±85 μl CO(2) frog(-1) freezing event(-1)) and after the frog has thawed (564±75 μ l CO(2) frog(-1) freezing event(-1)). We interpret these increases in metabolic rate to represent the energetic costs of preparation for freezing, the response to freezing and the re-establishment of homeostasis and repair of damage after thawing, respectively. We assumed that frogs metabolise lipid when unfrozen and that carbohydrate fuels metabolism during cooling, freezing and thawing, and when frozen. We then used microclimate temperature data to predict overwinter energetics of wood frogs. Based on the freezing and melting points we measured, frogs in the field were predicted to experience as many as 23 freeze-thaw cycles in the winter of our microclimate recordings. Overwinter carbohydrate consumption appears to be driven by the frequency of freeze-thaw events, and changes in overwinter climate that affect the frequency of freeze-thaw will influence carbohydrate consumption, but changes that affect mean temperatures and the frequency of winter warm spells will modify lipid consumption.
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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) | 0.000 | 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".