Oxygen consumption rate during recovery from loss of equilibrium induced by warming, hypoxia, or exhaustive exercise in rainbow darter (<scp><i>Etheostoma caeruleum</i></scp>)
Bibliographic record
Abstract
Abstract Animals routinely encounter environmental (e.g., high temperatures and hypoxia) as well as physiological perturbations (e.g., exercise and digestion) that may threaten homeostasis. However, comparing the relative threat or “disruptiveness” imposed by different stressors is difficult, as stressors vary in their mechanisms, effects, and timescales. We exploited the fact that several acute stressors can induce the loss of equilibrium (LOE) in fish to (i) compare the metabolic recovery profiles of three environmentally relevant stressors and (ii) test the concept that LOE could be used as a physiological calibration for the intensity of different stressors. We focused on Etheostoma caeruleum, a species that routinely copes with environmental fluctuations in temperature and oxygen and that relies on burst swimming to relocate and avoid predators, as our model. Using stop‐flow (intermittent) respirometry, we tracked the oxygen consumption rate (MO2) as E. caeruleum recovered from LOE induced by hypoxia (PO2 at LOE), warming (critical thermal maximum, CTmax), or exhaustive exercise. Regardless of the stressor used, E. caeruleum recovered rapidly, returning to routine MO2 within ~3 h. Fish recovering from hypoxia and warming had similar maximum MO2, aerobic scopes, recovery time, and total excess post‐hypoxia or post‐warming oxygen consumption. Though exhaustive exercise induced a greater maximum MO2 and corresponding higher aerobic scope than warming or hypoxia, its recovery profile was otherwise similar to the other stressors, suggesting that “calibration” to a physiological state such as LOE may be a viable conceptual approach for investigators interested in questions related to multiple stressors, cross tolerance, and how animals cope with challenges to homeostasis.
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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".