Phylogenies in yeast species descriptions: In defense of neighbor‐joining
Why this work is in the frame
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Bibliographic record
Abstract
The neighbor-joining (NJ) method of tree inference is examined, with special attention to its use in yeast species descriptions. How the often-vilified method works is often misunderstood. More importantly, given the right kind of data, its output is a phylogram that illustrates a hypothetical phylogeny that is just as credible as that obtained by any other method. And as with any other method, the result is greatly affected by sampling intensity, particularly the number of aligned positions used for analysis. I address various allegations, including the claim that the method is phenetic, and, therefore, not phylogenetic. I argue that NJ is the most suitable tree inference method to use in yeast species descriptions, primarily because it is best at visually preserving the extent of sequence divergence between close relatives, which continues to be the primary criterion for yeast species delineation. The relevance of bootstraps in the application of the phylogenetic species concept is discussed.
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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 | 0.000 |
| Open science | 0.000 | 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