Assessing the impact of heatwave exposure on the swimming performance, kinematics, and metabolism of a nearshore marine fish, Cymatogaster aggregata
Bibliographic record
Abstract
The severity and frequency of marine heatwaves (MHWs) have increased drastically across the globe, with some of the most intense heatwaves happening within the last decade. The consequences of MHWs vary in severity and include range shifts and diet changes as well as mortality events in marine species. In fishes, elevated temperatures can lead to changes in whole-animal metabolism and performance metrics. However, the impact of temperature on a key performance metric, optimal swim speed (Uopt) is not fully understood. Here, we investigate how heatwave exposure (I.e., +2°C and +4°C) over a five-day period affects the metabolic rate and swimming performance of the shiner perch (Cymatogaster aggregata) using a swimming respirometer. Preliminary findings demonstrated that Uopt slightly increased following moderate MHW exposure (+2°C) and plateaued at higher MHW exposure (+4°C). However, metabolic costs and maximum swimming speed peaked following moderate MHW exposure and declined under more intense MHWs. Not only does this provide more information on the swimming energetics and performance of a common nearshore species, but it also provides insight into heat tolerance and how this species may respond to future projected marine heatwaves.
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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".