A practical algorithm for recovering the best supported edges of an evolutionary tree (extended abstract)
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Bibliographic record
Abstract
) Vincent Berry David Bryant y Tao Jiang z Paul Kearney x Ming Li -- Todd Wareham k Haoyong Zhang Abstract It is now routine for biologists to conduct evolutionary analyses of large DNA and protein sequence datasets. A computational bottleneck in these analyses is the recovery of the topology of the evolutionary tree for a set of sequences. This paper presents a practical solution to this challenging problem. More specifically, an algorithm called hypercleaning is presented that efficiently reconstructs from the sequence data the best supported edges of the evolutionary tree. This algorithm is a substantial improvement over previous algorithms in its ability to recover edges of the evolutionary tree. Hypercleaning also incorporates a detailed error model that relates errors in Address: D'epartement d'Informatique Fondamentale et Applications, LIRMM, Universit'e de Montpellier II, France. Part of this work was done at the D'epartement de Math'ematiques,EURISE, Universit'e ...
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it