The Probability of Correctly Resolving a Split as an Experimental Design Criterion in Phylogenetics
Bibliographic record
Abstract
We illustrate how recently developed large sequence-length approximations to probabilities of correct phylogenetic reconstruction for maximum likelihood estimation can be used to evaluate experimental design strategies. The specific criterion of interest is the probability of correctly resolving an a priori defined split of interest in a phylogenetic tree. Design strategies considered include increased taxon sampling and increasing sequence length. Our analyses of specific examples strongly suggest that it is better to sample taxa that connect as close as possible to the split of interest. Assuming this can be done, these examples suggest it is better to sample additional taxa than to add a comparable number of sites for the existing taxa. If the rates of evolution in the added taxa are slow, it is better to choose taxa connecting to a long edge, but if rates are comparable to a sister lineage, it is not necessarily the best strategy to sample taxa connected to a long edge. We also examined deleting taxa while increasing the number of sites. Although deleting a small number of taxa distant from the split of interest can be beneficial, deleting too many or making poor choices as to what should be deleted can lead to smaller probabilities of correct reconstruction than for the original sequence data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.126 | 0.390 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".