A Reexamination of Peto’s Paradox: Insights Gained from Human Adaptation to Varied Levels of Ionizing and Non-ionizing Radiation
Why this work is in the frame
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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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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 it