Bibliographic record
Abstract
What's the difference between a couch potato and a marathon runner? It's partly that the marathoner actually gets off the couch and runs, training him or herself to become a better runner. However, physiologists know that being a good athlete also depends on genes. Genetics can contribute up to half of the variation seen in human exercise capacity, and some other species have evolved the ability to perform amazing feats of athleticism, often with very little training. It is because exercise capacity has a genetic basis that it can evolve, and can confer a strong advantage to animals in the wild. To investigate further, Norberto Gonzalez from the University of Kansas and his colleagues focus in a recent paper on the evolution of running endurance in rats.Transporting oxygen to mitochondria in exercising muscles is extremely important for making energy in the form of ATP. Animals with greater exercise capacity are generally better at transporting O2 along the pathway from environment to mitochondria, and this pathway has many steps:O2 is brought into the lungs with each breath, where it diffuses into the blood and is delivered throughout the body, then finally diffuses to mitochondria in the tissues. It was unclear how each step of the O2transport pathway evolves in athletic species, which led Gonzalez and his team to find out more by artificially selecting rats for running endurance.Artificial selection is a way of mimicking natural selection in the lab. Every generation, individual rats with the best running endurance were selected and bred together, generating high endurance populations that could run much further than rats with low running endurance. To understand the basis for these differences, Gonzalez and colleagues first measured the maximal rate of oxygen consumption(V̇O2max) during heavy exercise in rats after 15 generations of selective breeding. They found that the high endurance runners had a higher V̇O2max than rats in earlier generations, so they knew that the oxygen transport pathway was evolving. To find out what was causing the changes in V̇O2max, the authors analyzed each individual step in the O2 pathway. Both the rate of O2 delivery to the tissues by the blood and rate of O2 diffusion into the tissues from the blood were enhanced in high endurance runners, which explained their higher V̇O2max. All other steps in the O2 pathway were the same in high and low endurance runners.Gonzalez and colleagues made a remarkable finding when they compared these results to experiments in the same lines of rats after only seven generations of artificial selection: at this early stage of evolution, only the rates of O2 diffusion into the tissues were enhanced in high capacity runners. This discovery has important implications for how physiological systems evolve. It implies that when selection is applied to the O2transport pathway as a whole, different components of that pathway each evolve at a different pace. The authors conclude that the changes observed in tissue O2 diffusion at generation seven promoted changes in O2delivery at generation 15. In more general terms, evolution of the first physiological trait increased the selective advantage of the second trait,which later evolved as well. Gonzalez and colleagues have therefore shown us that the evolution of endurance capacity involves multiple physiological changes, and that there are many interesting differences between couch potatoes and marathon runners!
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".