Warming, but not acidification, increases metabolism and reduces growth of redfish (<i>Sebastes fasciatus</i>) in the Gulf of St. Lawrence
Bibliographic record
Abstract
Understanding the effects of global change, including temperature, pH, and oxygen availability, on commercially important species is crucial for anticipating consequences for these resources and their ecosystems. In the Gulf of St. Lawrence (GSL), redfish ( Sebastes spp.) were under moratorium from 1995 to 2023, but the fishery has reopened in 2024 following massive recruitment observed in 2011–2013. Despite current high abundance, little is known about their metabolic and thermal physiology. To address this, we quantified the effects of four acclimation temperatures (2.5, 5.0, 7.5, and 10.0 °C) and two ocean acidification scenarios (current and future) on standard and maximum metabolic rates (SMR and MMR), aerobic scope (AS), factorial aerobic scope, hypoxia tolerance (O2crit), food consumption, growth and food conversion efficiency (FCE) in redfish ( Sebastes fasciatus Storer, 1854). SMR, MMR, and AS increased with temperature, but growth and FCE decreased with temperature, likely due to increased cost of maintenance. Food consumption was lower at 2.5 °C, but similar at higher temperatures. Redfish were less hypoxia-tolerant at higher temperatures. Except for SMR, no significant effect of pH was observed. These results suggest that future changes in the GSL will challenge redfish, with potential long-term effects on growth due to increased energy requirements.
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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".