Impacts of elevated water temperature on the behaviour and thermal tolerance of the Bluntnose Minnow (<i>Pimephales notatus</i>)
Bibliographic record
Abstract
Fish often show behavioural responses to elevated water temperature before they reach their upper thermal limit. We measured the activity, aggression, and thermal tolerance (critical thermal maximum, CTmax; agitation temperature, Tag; thermal agitation window; and thermal safety margin) of the Bluntnose Minnow ( Pimephales notatus Rafinesque, 1820). Thirty fish underwent a 5-week acclimation period to two temperatures (ambient, 16 °C or hot, 25 °C). We took behavioural observations throughout the 5 weeks and conducted thermal tolerance trials at the end of the acclimation period. Aggression was marginally higher in fish acclimated to hot water, but there was no change over time (week 1 to 5). Activity was higher in the hot acclimation treatment, but again there was no change in activity over time. We found that CTmax, Tag, and the agitation window (the difference between CTmax and Tag) were significantly greater in fish acclimated to 25 °C, but the thermal safety margin (the difference between acclimation temperature and CTmax) was smaller. This study highlights the significance of behavioural responses to elevated water temperature that may occur well before the upper critical thermal limit, which may result in negative physiological (metabolic costs) or behavioural (avoidance/refuge seeking behaviour) impacts over time.
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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".