The physiological ups and downs of thermal variability in temperate freshwater ecosystems
Bibliographic record
Abstract
Freshwater fish face a variety of spatiotemporal thermal challenges throughout their life. On a broad scale, temperature is an important driver of physiological, behavioural and ecological patterns and ultimately affects populations and overall distribution. These broad patterns are partly underpinned by the small-scale local effects of temperature on individuals within the population. Climate change is increasing the range of daily thermal variation in most freshwater ecosystems, altering behaviour and performance of resident fishes. The aim of this review is understanding how daily thermal variation in temperate rivers affects individual fish physiology, behaviour and overall performance. The following are highlighted in this study: (a) the physical characteristics of rivers that can either buffer or exacerbate thermal variability, (b) the effects of thermal variability on growth and metabolism, (c) the approaches for quantifying thermal variation and thermal stress and (d) how fish may acclimatize or adapt to our changing climate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".