Construction of amino acid rate matrices and extensions of the Barry and Hartigan model for phylogenetic inference
Bibliographic record
Abstract
This thesis considers two distinct topics in phylogenetic analysis. The first is\nconstruction of empirical rate matrices for amino acid models. The second topic,\nwhich constitutes the majority of the thesis, involves analysis of and extensions to\nthe BH model of Barry and Hartigan (1987).\nThere are a number of rate matrices used for phylogenetic analysis including\nthe PAM (Dayhoff et al. 1979), JTT (Jones et al. 1992) and WAG (Whelan and\nGoldman 2001). The construction of each of these has difficulties. To avoid adjusting\nfor multiple substitutions, the PAM and JTT matrices were constructed using only\na subset of the data consisting of closely related species. The WAG model used\nan incomplete maximum likelihood estimation to reduce computational cost. We\ndevelop a modification of the pairwise methods first described in Arvestad and Bruno\nthat better adjusts for some of the sparseness difficulties that arise with amino acid\ndata.\nThe BH model is very flexible, allowing separate discrete-time Markov processes\nto occur along different edges. We show, however, that an identifiability\nproblem arises for the BH model making it difficult to estimate character state frequencies\nat internal nodes. To obtain such frequencies and edge-lengths for BH\nmodel fits, we define a nonstationary GTR (NSGTR) model along an edge, and find\nthe NSGTR model that best approximates the fitted BH model. The NSGTR model\nis slightly more restrictive but allows for estimation of internal node frequencies and interpretable edge lengths.\nWhile adjusting for rates-across-sites variation is now common practice in phylogenetic\nanalyses, it is widely recognized that in reality evolutionary processes can\nchange over both sites and lineages. As an adjustment for this, we introduce a BH\nmixture model that not only allows completely different models along edges of a\ntopology, but also allows for different site classes whose evolutionary dynamics can\ntake any form.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".