On the use of median‐joining networks in evolutionary biology
Why this work is in the frame
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Bibliographic record
Abstract
Median-joining (MJ) was proposed as a method for phylogeographical analysis and is enjoying increasing popularity. Herein, we evaluate the efficacy of the approach as originally intended. We show that median-joining networks (MJNs) are theoretically untenable for evolutionary inference, and that confusion has afflicted their use for over 15 years. The approach has two obvious shortcomings: its reliance on distance-based phenetics (overall similarity instead of character transformations) and the lack of rooting (no direction or history). Given that evolution involves both change and time, and the absence of rooting removes time (ancestor-descendant relationships) from the equation, the approach cannot yield defensible evolutionary interpretations. We also examine the impact of MJ analyses on evolutionary biology via an analysis of citations and conclude that the spread of MJNs through the literature is difficult to explain, especially given the availability of character-based analyses.
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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 | 0.001 | 0.001 |
| 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