Are we any closer to understanding why fish can die after severe exercise?
Bibliographic record
Abstract
Abstract Post‐exercise mortality (PEM) may occur when fish exercise to exhaustion and are pushed so far beyond their physiological limits that they can no longer sustain life. Although fish exercise to overcome a variety of natural challenges, the phenomenon of PEM is most often observed as the result of interactions between fish and humans. The seminal work of Black (Can J Fish Aquat Sci, 15:573, 1958) and Wood et al. (J Fish Biol, 22:189, 1983) provided a foundation for exploring the potential causes of PEM in fish. With no “silver bullet” explaining PEM being apparent, contemporary research has continued to focus on physiological mechanisms of exhaustion in fish, including factors such as oxygen delivery, ion regulation, hormone signalling, and cardiac function. This paper provides an overview of these studies, and reviews the continuous improvement in data collection methods, tools, and experimental protocols used to examine the PEM phenomenon. These studies of exhaustion have played an important role in informing management actions for activities such as bycatch revival and fish passage. Since the contribution of Wood et al. (Journal of Fish Biology, 22(2):189–201, 1983), the combined efforts of fundamental and applied research have yielded a greater understanding of why fish die after severe exercise, yet much remains to be explored through future work.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".