The environmental context of inducible HSP70 expression in Eastern Brook Trout
Bibliographic record
Abstract
Abstract Much research has focused on the population-level effects of climate change on Eastern Brook Trout (Salvelinus fontinalis). While some studies have considered here sub-lethal stress caused by warming waters, the role of multiple, interacting stressors remains largely unexplored. We used inducible heat shock protein 70 (HSP70) as a molecular biomarker to assess in situ response of Eastern Brook Trout in headwater streams to multiple potential stressors, including temperature. Over 7 sampling events during 2018 and 2019, we sampled 141 fish and found that HSP70 expression and 3-day mean water temperature exhibited a quadratic relationship (R2-adj = 0.68). Further analyses showed that HSP70 expression was explained by temperature, relative water level and their interaction (R2-adj = 0.75), while fish size and capture location were not factors. We observed a significant increase in HSP70 expression during periods of low relative water level with warm temperatures (~18°C) and also during high relative water level with cold temperatures (~8°C). Our results suggest that temperatures at the edges of the preferred range coupled with relative water level might act together to trigger the cellular stress response in Eastern Brook Trout and that there is greater variation in response at colder temperatures. These findings reinforce the need to consider complex, interactive stressors in influencing the health and persistence of Eastern Brook Trout populations into the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".