Distance Computation in the Space of Phylogenetic Trees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A phylogenetic tree represents the evolutionary history of a set of organisms. There are many different methods to construct phylogenetic trees from biological data. To either compare one such algorithm with another, or to find the likelihood that a certain tree is generated from the data, researchers need to be able to compute the distance between trees. In 2001, Billera, Holmes, and Vogtmann introduced a space of phylogenetic trees, and defined the distance between two trees to be the length of the shortest path between them in that space. We use the combinatorial and geometric properties of the tree space to develop two algorithms for computing this geodesic distance. In doing so, we show that the possible shortest paths between two trees can be compactly represented by a partially ordered set. We calculate the shortest distance between the start and target trees for each potential path by converting the problem into one of finding the shortest path through a certain subspace of Euclidean space. In particular, we show there is a linear time algorithm for finding the shortest path between a point in the all positive orthant and a point in the all negative orthant of R^k contained in the subspace of R^k consisting of all orthants with the first i coordinates non- positive and the remaining coordinates non-negative for 0 <= i <= k. This case is of interest, because the general problem of finding a shortest path through higher dimensional Euclidean space with obstacles is NP-hard. The resulting algorithms for computing the geodesic distance appear to be the best available to date.
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.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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