Impact of ionizing radiation on the environmental microbiomes of Chernobyl wetlands
Bibliographic record
Abstract
Abstract Radioactive contamination in the form of ionizing radiation can be a devastating pollutant because it has the potential to cause damage to DNA and other biomolecules. Anthropogenic sources of ionizing radiation include accidents in nuclear power plants, such as the one in Chernobyl 1986, which caused long-term radioactive pollution. Studies on animals within radioactive zones have provided us with a greater understanding of how wildlife can persevere despite chronic radiation exposure, however, we still know very little about the effects of radiation on the microbial communities in the environment. Here, we examined the impact of ionizing radiation and other environmental factors on the diversity and composition of environmental microbiomes in the wetlands of Chernobyl. We combined extensive field sampling along a gradient of radiation together with 16S rRNA high-throughput metabarcoding (Illumina NovaSeq). While radiation did not affect the alpha diversity of the microbiomes in sediment, soil, or water, it had a strong effect on the beta diversity, indicating that the microbial composition was affected by ionizing radiation. Specifically, we detected several microbial taxa that were more abundant in areas with high radiation levels within the Chernobyl Exclusion Zone, including bacteria and archaea known to be radioresistant. Overall, our results reveal the existence of rich and diverse microbiomes in Chernobyl wetlands, with multiple taxonomic groups that are able to thrive despite the radioactive contamination. Further field and laboratory-based approaches will help to forecast the functionality and re-naturalization dynamics of radiocontaminated environments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".