An improved method for fitting gamma distribution to substitution rate variation among sites
Bibliographic record
Abstract
Abstract Gamma distribution has been used to fit substitution rate variation over site. One simple method to estimate the shape parameter of the gamma distribution is to 1) reconstruct a phylogenetic tree and the ancestral states of internal nodes, 2) perform pairwise comparison between nodes on each side of each branch to count the number of “observed” substitutions for each site, and apply correction of multiple hits to derive the estimated number of substitutions for each site, and 3) fit the site-specific substitution data to gamma distribution to obtain the shape parameter α This method is fast but its accuracy depends much on the accuracy of the estimated site-specific number of substitutions. The existing method has three shortcomings. First, it uses Poisson correction which is inadequate for almost any nucleotide sequences. Second, it does independent estimation for the number of substitutions at each site without making use of information at all sites. Third, the program implementing the method has never been made publically available. I have implemented in DAMBE software a new method based on the F84 substitution model with simultaneous estimation that uses information from all sites in estimating the number of substitutions at each site. DAMBE is freely available at available at http://dambe.bio.uottawa.ca
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".