Oxidative Ecology of Wild Fish: Investigating the Effects of Intrinsic and Extrinsic Factors on Oxidative Stress and Its Link to Life-Histories
Bibliographic record
Abstract
Though oxidative stress has been reported to have important repercussions on the biology and ecology of animals, most research has thus far focused on mammals and birds, while relatively little is known about its role in fish.Fish display a wide range of life history strategies, for which resources must be allocated delicately, providing the perfect opportunity to investigate the effects of extrinsic (i.e., environmental) and intrinsic (i.e., life-histories) factors on oxidative stress, as well as the role of oxidative stress markers in determining life-history strategies.In Chapter 2, I reviewed the current literature on oxidative stress in fish from an ecological perspective, and found that oxidative ecology in fish is largely understudied.In Chapter 3, I assessed the short-term (two-weeks) and long-term (4 months) effects of cortisol on oxidative status in brown trout (Salmo trutta) from a Danish lowland stream, and demonstrated that cortisol caused an increase in glutathione in the short-term, and that oxidative stress levels as well as low molecular weight antioxidants decreased in the short-term in all treatments.No effects of treatment or time were detected in the long-term.Interestingly, I show that overwinter survival may be associated with low total glutathione and low oxidative stress levels.In Chapter 4, I investigated the oxidative status of migratory and resident individuals from the same Danish brown trout (S. trutta) population, and demonstrated that migratory individuals have a higher antioxidant capacity than their resident counterparts, which may represent an anticipatory response to the upcoming demanding journey that migration represents.Continuing studies on the oxidative ecology of fish may help to uncover the physiological mechanisms that influence behavior in relation to ecological phenomena.A special thank you goes to my co-supervisor, Steven J
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".