Acute critical thermal maximum does not predict chronic incremental thermal maximum in Atlantic salmon (Salmo salar)
Bibliographic record
Abstract
Atlantic salmon is an important aquaculture species farmed in ocean net-pens and therefore subjected to changing environmental conditions, including rising temperatures. This creates a need for research on the thermal tolerance of this species for the future of sustainable aquaculture. We investigated the thermal tolerance of individually tagged Atlantic salmon post-smolts subjected sequentially to two common high-temperature challenges: critical thermal maximum (CTmax) followed by incremental thermal maximum (ITmax). Our goals were (1) to determine whether CTmax can predict ITmax for individual fish, and (2) to examine connections between various body size (mass, length, condition factor), cardiac (absolute and relative ventricle mass) and blood (hematocrit) metrics and thermal tolerance. We found no relationship between CTmax and ITmax. This is of concern because CTmax, which is a quick and easy test, is often used to predict upper lethal limits in fish despite not using real-world rates of temperature increase and not using death as the experimental endpoint (unlike ITmax). Also, some metrics which correlated in one direction with CTmax had the opposite correlation with ITmax. For instance, smaller fish or fish with smaller ventricles had a higher CTmax but a lower ITmax than larger fish or fish with larger ventricles. Taken together, these results highlight the need to take care when using acute thermal tolerance tests to predict real-world responses to rising temperatures.
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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 | 0.001 |
| 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".