Moving forward in determining the causes of mutations: the features of plants that make them suitable for assessing the impact of environmental factors and cell age
Bibliographic record
Abstract
Currently, the types of factors that impact the mutation rate is a controversial issue. The marked attention towards identifying the factors that impact the genomic mutation rate is justified because mutations are the source of genetic variation underlying evolution and because many mutations have deleterious effects and can cause diseases. Although data showing correlations between germ cell division number and mutation rates (from epidemiological studies and molecular evolutionary rate analyses) have suggested that most mutations in animals are replication errors, this notion is highly debated and inconsistencies in the correlations suggest that other, replication-independent factors, could play an important role. Likely candidates include environmental parameters and cell age, but these issues have proved to be difficult to study using animals and in vitro systems, and consequently, very few or no data currently exist. The specific features of plants that make them powerful model systems for revealing the influence of the environment (natural environmental factors) and cell age on the spontaneous genomic mutation rate are discussed here. Overall, the evidence suggests that plants could be key biological systems for advancing our knowledge about how and why heritable mutations arise.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".