Bibliographic record
Abstract
Oxygen is essential for life, providing the energy needed for surviving and thriving in our world. However, animals that live in aquatic habitats have their work cut out for them. Not only is oxygen less freely available in water than in terrestrial environments but also, with climate change, bouts of low oxygen (hypoxia) are becoming more and more common in these habitats. For decades, scientists have been studying and categorizing how well fish can handle low oxygen (or not) to understand who the ‘winners’ and ‘losers’ would be in an oxygen-limited world. The ultimate goal for many scientists is to create rules that allow us to predict which species are more vulnerable to changing environments and whether this vulnerability will change their current distributions around the globe.Until recently, scientists have been studying how one or two environmental factors in isolation can affect oxygen limitation – but many factors can conspire to impact a fish's oxygen supply or demand. For example, low oxygen levels in water frequently occur when temperatures are elevated. This increases a fish's demand for oxygen and effectively makes it worse at dealing with limited oxygen availability. However, there's conflicting evidence for how other factors that influence oxygen demand, such as body size, affect a fish’s tolerance of low-oxygen scenarios within and across species.To approach this in a more holistic manner, Wilco Verberk from Radbound University, The Netherlands, and colleagues from Chile, the UK and France, searched the literature and an existing database of limiting oxygen levels for what factors might influence fishes’ ability to withstand low oxygen. Compiling traits for 195 fish species, they took into account the many physical factors that relate to the fishes’ oxygen supply or demand and investigated whether these factors work separately or together to explain why some fishes are better than others at dealing with low oxygen.Verberk and colleagues found that the relatedness of species heavily influences their ability to tolerate low oxygen. For example, sharks and rays are more sensitive to low oxygen than their distantly related ‘cousins’ goldfish, which are renowned low-oxygen champions. Salinity is also a predictor of how well fishes deal with limited oxygen availability, wherein freshwater fishes are less sensitive to hypoxia than marine fishes. This is unsurprising as freshwater habitats tend to have more intense fluctuations in oxygen and temperature than marine habitats and so species that live in freshwater need to be better prepared to withstand frequent bouts of low oxygen.The scientists’ most interesting finding was that bigger fishes and fishes with bigger cell sizes are more vulnerable to low oxygen, specifically in warm habitats. At higher temperatures, their demand for oxygen is high but they might be limited by the flow of oxygen into the blood, making them more sensitive to low-oxygen situations. In cooler environments, fishes with larger bodies and bigger cell sizes are actually less vulnerable to low oxygen than their small counterparts. Colder water has a higher viscosity – it's thicker and stickier – which would make it more difficult for small fishes to breathe, giving larger fishes the leg (or fin?) up.Low oxygen and warmer temperatures are predicted to work together to limit desirable habitats and reshape species distribution. Yet, other environmental and biological factors can impact oxygen supply or demand. By including body size in their considerations, Verberk and colleagues were able to resolve how body mass impacts hypoxia tolerance and add to the growing body of work seeking to predict fishes’ vulnerability in a changing world.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".