Impact of hypercapnic incubation on hatchling common snapping turtle ( <i>Chelydra serpentina</i> ) growth and metabolism (1101.3)
Bibliographic record
Abstract
Some reptilian embryos are naturally exposed to low levels of O 2 and high levels of CO 2 . This phenomenon has provoked much research on the physiological effects of developmental hypoxia in reptiles and associated implications on survival. However, notably less attention has been given to the effects of hypercapnia. In this study, we investigated the growth trajectories and metabolic rates (resting and postprandial) of common snapping turtle ( Chelydra serpentina ) hatchlings that were chronically incubated as embryos in one of three environmental conditions: Atmospheric CO 2 (control); 3.5% CO 2 (3.5%); and 7% CO 2 (7%). Control and 7% hatchlings had similar growth trajectories and mean masses. However, the 3.5% animals had both a higher mean mass and a steeper trajectory compared to the other conditions. VO 2 experiments revealed that resting metabolic rate was significantly elevated in the hypercapnic conditions compared to the control condition. Moreover, the postprandial metabolic rate was 1.6‐ to 1.7‐fold higher than resting in the hypercapnic, compared to a 2‐fold increase in the normal incubated turtles. This study not only demonstrates that snapping turtles can survive considerable hypercapnia during embryonic development, but also that incubation condition has lasting effects on physiology post‐hatching. Grant Funding Source : Supported by NSF CAREER IBN IOS‐0845741 to DAC
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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".