Positive or negative? The shell alters the relationship among behavioral defense strategy, energy metabolic levels and antioxidant capacity in freshwater turtles
Bibliographic record
Abstract
BACKGROUND: The relationships among energy metabolic levels, behavioral and other physiological traits help to determine the trade-off of energy allocation between different traits and the evolution of life-history driven by natural selection. However, these relationships may be distinctive in selected animal taxa because of their unique traits. In the present study, the relationships among energy metabolic levels, behavioral defense strategies, and antioxidant capacity were explored in three freshwater turtle species with different shell morphologies, by assessing responses to attack, righting time, shell morphology, whole-organism metabolic rates, tissue metabolic enzyme activities and antioxidant levels. RESULTS: These latter two species both showed an active defense strategy, a higher factorial aerobic scope and better muscle anaerobic metabolic capacity but relatively smaller shells, lower RMR and antioxidant capacity CONCLUSION: Our results indicate a negative relationship between RMR and activity levels in behavioral defense strategies along small-big shell continuum among the three turtle species. We also found a positive relationship between antioxidant capacity and energy metabolism but a negative one between antioxidant capacity and activity levels in defense strategies. The present study indicated a role of turtle shell in forming unique relationship between energy metabolic levels and behaviors in freshwater turtle taxa and a possible trade-off between the maintenance of physiological homeostasis and activity levels in energy allocation.
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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) | 0.002 | 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".