Bibliographic record
Abstract
Physiology can be viewed as the collection of mechanisms and processes that allows organisms to deal with challenges from both internal (e.g., exercise, growth, reproduction) and external (e.g., variations in temperature, oxygen and water availability, salinity, pressure, radiation, heavy metals, etc.) sources. In this chapter we focus on solutions to some of the external challenges to life in extreme environments. This subject is a huge one because life on Earth has radiated into every conceivable environment, from the frigid Antarctic to boiling hot springs, from the ocean depths to the tops of mountains, from hypersaline lakes to the driest deserts, and many more. We mainly consider biochemical and molecular solutions by vertebrate animals to environmental challenges of low oxygen and low temperature because these hold lessons that can be applied to the human condition and medical concerns. However, the reader should be aware that the extremes of vertebrate life are bested on every front by the capabilities of invertebrates, plants, bacteria, and archaea and many excellent resources explore life at the extremes from different perspectives; selected texts include those by Hochachka and Somero (1), Schmid-Neilsen (2), Ashcroft (3), Margesin and Schinner (4), Willmer et al. (5), Lutz et al. (6), and Gerday and Glansdorff (7).
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 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) | 0.000 | 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".