State-of the art methodologies dictate new standards for phylogenetic analysis
Bibliographic record
Abstract
The intention of this editorial is to steer researchers through methodological choices in molecular evolution, drawing on the combined expertise of the authors. Our aim is not to review the most advanced methods for a specific task. Rather, we define several general guidelines to help with methodology choices at different stages of a typical phylogenetic 'pipeline'. We are not able to provide exhaustive citation of a literature that is vast and plentiful, but we point the reader to a set of classical textbooks that reflect the state-of-the-art. We do not wish to appear overly critical of outdated methodology but rather provide some practical guidance on the sort of issues which should be considered. We stress that a reported study should be well-motivated and evaluate a specific hypothesis or scientific question. However, a publishable study should not be merely a compilation of available sequences for a protein family of interest followed by some standard analyses, unless it specifically addresses a scientific hypothesis or question. The rapid pace at which sequence data accumulate quickly outdates such publications. Although clearly, discoveries stemming from data mining, reports of new tools and databases and review papers are also desirable.
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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 | 0.052 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.009 | 0.022 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.006 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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".