Estimating numbers of EMS-induced mutations affecting life history traits in <i>Caenorhabditis elegans</i> in crosses between inbred sublines
Bibliographic record
Abstract
Inbred lines of the nematode Caenorhabditis elegans containing independent EMS-induced mutations were crossed to the ancestral wild-type strain (N2). Replicated inbred sublines were generated from the F1 offspring under conditions of minimal selection and, along with the N2 and mutant progenitor lines, were assayed for several fitness correlates including relative fitness (w). A modification of the Castle-Wright estimator and a maximum-likelihood (ML) method were used to estimate the numbers and effects of detectable mutations affecting these characters. The ML method allows for variation in mutational effects by fitting either one or two classes of mutational effect, and uses a Box-Cox power transformation of residual values to account for a skewed distribution of residuals. Both the Castle-Wright and the ML analyses suggest that most of the variation among sublines was due to a few (approximately 1.5-2.5 on average) large-effect mutations. Under ML, a model with two classes of mutational effects, including a class with small effects, fitted better than a single mutation class model, although not significantly better. Nonetheless, given that we expect there to be many mutations induced per line, our results support the hypothesis that mutations vary widely in their effects.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".