Physiological consequences of varying large shark exposure on striped bass (<i>Morone saxatilis</i>)
Bibliographic record
Abstract
Large marine predators often aggregate seasonally in discrete locations to take advantage of optimal foraging conditions, leading to spatial and temporal variation in their exposure on other species. However, our understanding of the impacts this exposure may have on the behavior and physiology of prey is poor, especially in marine systems. Here, we evaluated the non-consumptive effects of potential exposure to large sharks (white sharks, Carcharodon carcharias (Linnaeus, 1758)) on the stress physiology of an economically important teleost, the striped bass (Morone saxatilis (Walbaum, 1792)), off Cape Cod, Massachusetts, USA. We sampled fish in habitats that varied significantly in shark exposure across 5 months and over 2 years, evaluating blood physiology stress indicators (i.e., cortisol, glucose, and lactate concentrations) and reflex impairment. None of the blood parameters were influenced by shark exposure, although we did observe subtle temperature and seasonal effects. One of the three reflex tests (the vertical orientation test) was negatively affected by shark exposure, although the mechanistic basis for this finding is unclear. This work supports the notion that predictable sources of predation pressure tend not to manifest in stress-related costs in free-ranging prey, which has implications for shaping our understanding of how large sharks influence ecosystems through non-consumptive effects.
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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".