A Reexamination of Peto’s Paradox: Insights Gained from Human Adaptation to Varied Levels of Ionizing and Non-ionizing Radiation
Bibliographic record
Abstract
Humans have generally evolved some adaptations to protect against UV and different levels of background ionizing radiation. Similarly, elephants and whales have evolved adaptations to protect against cancer, such as multiple copies of the tumor suppressor gene p53, due to their large size and long lifespan. The difference in cancer protection strategies between humans and elephants/whales depends on genetics, lifestyle, environmental exposures, and evolutionary pressures. In this paper, we discuss how the differences in evolutionary adaptations between humans and elephants could explain why elephants have evolved a protective mechanism against cancer, whereas humans have not. Humans living in regions with high levels of background radiation, e.g. in Ramsar, Iran where exposure rates exceed those on the surface of Mars, seem to have developed some kind of protection against the ionizing radiation. However, humans in general have not developed cancer-fighting adaptations, so they instead rely on medical technologies and interventions. The difference in cancer protection strategies between humans and elephants/whales depends on genetics, lifestyle, environmental exposures, and evolutionary pressures. In this paper, we discuss how the differences in evolutionary adaptations between humans and elephants could explain why elephants have evolved a protective mechanism against cancer, whereas humans have not. Studying elephant adaptations may provide insights into new cancer prevention and treatment strategies for humans, but further research is required to fully understand the evolutionary disparities.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".