Kinematic performance and muscle activation patterns during post-freeze locomotion in the Wood Frog (<i>Rana sylvatica</i>)
Bibliographic record
Abstract
Wood Frogs (Rana sylvatica LeConte, 1825 = Lithobates sylvaticus (LeConte, 1825)) exhibit one of the most extreme freeze tolerance responses found in vertebrates. While extensive work is continuing to resolve the physiological mechanisms involved, few have studied the effects of freezing on locomotor performance. The ability to mount an appropriate locomotor response is vital, as locomotion can affect both survivorship and reproductive success. To investigate how the biomechanical processes during locomotion are altered following freezing, stroke cycle timings and kinematic performance were measured prior to and immediately following a freeze–thaw cycle. Additionally, the effects of cooling rate (0.3 versus 0.8 °C/h) were also assessed. While jumping and swimming performance were both reduced post-freeze, the effects were more pronounced during swimming, with observed reductions in velocity and distance travelled. Interestingly, these changes occurred largely independent of cooling rate. Altered stroke cycle timings and highly variable muscle activation/deactivation patterns suggest an impairment in muscle function as frogs continued to recover from the effects of freezing. This was supported by the physiology of frogs post-freeze, specifically, the persistence of elevated glucose levels in muscles important during locomotion. Collectively, these findings suggest that reductions in locomotor performance observed immediately following a freeze–thaw cycle are driven by alterations in muscle function.
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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 | 0.000 | 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 | 0.000 |
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 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".