Bibliographic record
Abstract
For those of us that are insulated from the vagaries of the climate by our ability to generate our own warmth, it can be hard to understand the physiological challenges faced by ectothermic animals, which depend on their surroundings to maintain their body temperature. And for aquatic ectotherms that are used to a specific range of comfortable temperatures, the challenges might just be about to get more serious. Tony Hickey from the University of Auckland, New Zealand, explains that as temperatures rise, fish are at risk of heart failure and it was thought that the failure of the mitochondria that power muscle contraction might contribute. Hickey and his colleague Fathima Iftikar teamed up with researchers from Australia and Canada to compare how the hearts and the energy-generating mitochondria of three species of wrasse that inhabit different environments (tropical, temperate and cold temperate) cope in warmer waters (p. 2348). Gently increasing the temperature of the fish's water, Iftikar monitored the fish's heart rates and found that the tropical species (Thalassoma lunare) – which inhabits the narrowest thermal range – was most impacted by a rise in temperature. The power-producing mitochondria also began to fail before all three species experienced full heart failure as the temperature rose. In addition, she found that the fish's mitochondria were impacted at different points in the energy-generation process, indicating that the mechanism of mitochondrial failure was different in each species. Suggesting that temperature limitations on mitochondrial function might restrict the thermal ranges that wrasse occupy, the team concludes by saying, ‘Understanding mitochondrial function, or dysfunction, in ectotherms such as fish still requires study… to better understand the potential ramifications of 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.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".