Bibliographic record
Abstract
Previous article FreeCorrectionJonathan A. Mee and Sam YeamanJonathan A. Mee1. Department of Biology, Mount Royal University, Calgary, Canada Search for more articles by this author and Sam Yeaman2. Department of Biological Sciences, University of Calgary, Calgary, Canada Search for more articles by this author Original articleUnpacking Conditional Neutrality: Genomic Signatures of Selection on Conditionally Beneficial and Conditionally Deleterious MutationsPDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreIn the description of the methods for “Unpacking Conditional Neutrality: Genomic Signatures of Selection on Conditionally Beneficial and Conditionally Deleterious Mutations” by Mee and Yeaman (American Naturalist 194:529–540), it was erroneously stated that “All mutations were codominant (h=0.5), and mutation effects were additive.” This should have stated that fitness was calculated multiplicatively in the simulations. The difference between home and away fitness shown in all figures was calculated additively using the results of simulations that had been run with multiplicative fitness, so this could have introduced some slight errors. We checked a set of parameters most likely to generate substantial discordance (higher mutation rate and a range of selection coefficients) and found that the likely errors induced by this accounting mistake were small (fig. C1), as most mutations were of small effect, causing only small discrepancies between these two approaches to calculating fitness.Figure C1. Discrepancy between results for difference between home and away fitness with fitness calculated additively as in the original figures (black x’s) compared with the correct fitness calculation, where fitness is calculated multiplicatively (gray circles). In these simulations, N=1,000, m=0.01, μ=10−8, and s has the same range of values used in the original article (largest s=−0.064, smallest s=−0.000015625). These parameters are a subset of those shown in figure 2 of the original article.View Large ImageDownload PowerPoint Previous article DetailsFiguresReferencesCited by The American Naturalist Volume 194, Number 5November 2019 Published for The American Society of Naturalists Article DOIhttps://doi.org/10.1086/705837 HistorySubmitted August 01, 2019Published online September 18, 2019 © 2019 by The University of Chicago. All rights reserved.PDF download Crossref reports no articles citing this article.Related articlesUnpacking Conditional Neutrality: Genomic Signatures of Selection on Conditionally Beneficial and Conditionally Deleterious Mutations5 Apr 2019The American Naturalist
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.712 | 0.534 |
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".