What do warming waters mean for fish physiology and fisheries?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environmental signals act primarily on physiological systems, which then influence higher-level functions such as movement patterns and population dynamics. Increases in average temperature and temperature variability associated with global climate change are likely to have strong effects on fish physiology and thereby on populations and fisheries. Here we review the principal mechanisms that transduce temperature signals and the physiological responses to those signals in fish. Temperature has a direct, thermodynamic effect on biochemical reaction rates. Nonetheless, plastic responses to longer-term thermal signals mean that fishes can modulate their acute thermal responses to compensate at least partially for thermodynamic effects. Energetics are particularly relevant for growth and movement, and therefore for fisheries, and temperature can have pronounced effects on energy metabolism. All energy (ATP) production is ultimately linked to mitochondria, and temperature has pronounced effects on mitochondrial efficiency and maximal capacities. Mitochondria are dependent on oxygen as the ultimate electron acceptor so that cardiovascular function and oxygen delivery link environmental inputs with energy metabolism. Growth efficiency, that is the conversion of food into tissue, changes with temperature, and there are indications that warmer water leads to decreased conversion efficiencies. Moreover, movement and migration of fish relies on muscle function, which is partially dependent on ATP production but also on intracellular calcium cycling within the myocyte. Neuroendocrine processes link environmental signals to regulated responses at the level of different tissues, including muscle. These physiological processes within individuals can scale up to population responses to climate change. A mechanistic understanding of thermal responses is essential to predict the vulnerability of species and populations to climate change.
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.
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 | 0.000 | 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 it