Ionizing radiation affects the demography and the evolution of Caenorhabditis elegans populations
Bibliographic record
Abstract
Ionizing radiation can reduce survival, reproduction and affect development, and lead to the extinction of populations if their evolutionary response is insufficient. However, demographic and evolutionary studies on the effects of ionizing radiation are still scarce. Using an experimental evolution approach, we analyzed population growth rate and associated change in life history traits across generations in Caenorhabditis elegans populations exposed to 0, 1.4, and 50.0 mGy.h−1 of ionizing radiation (gamma external irradiation). We found a higher population growth rate in the 1.4 mGy.h−1 treatment and a lower in the 50.0 mGy.h−1 treatment compared to the control. Realized fecundity was lower in both 1.4 and 50.0 mGy.h−1 than control treatment. High irradiation levels decreased brood size from self-fertilized hermaphrodites, specifically early brood size. Finally, high irradiation levels decreased hatching success compared to the control condition. In reciprocal-transplant experiments, we found that life in low irradiation conditions led to the evolution of higher hatching success and late brood size. These changes could provide better tolerance against ionizing radiation, investing more in self-maintenance than in reproduction. These evolutionary changes were with some costs of adaptation. This study shows that ionizing radiation has both demographic and evolutionary consequences on populations.
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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 | 0.000 | 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 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".