A COLLAPSING METHOD FOR THE EFFICIENT RECOVERY OF OPTIMAL EDGES IN PHYLOGENETIC TREES
Bibliographic record
Abstract
As the amount of sequencing efforts and genomic data volume continue to increase at an accelerated rate, phylogenetic analysis provides an evolutionary context for understanding and interpreting this growing set of complex data. We introduce a novel quartet based method for inferring molecular based phylogeny called hypercleaning* (HC*). The HC* method is based on the hypercleaning (HC) technique,2 which possesses an interesting property of recovering edges (of a phylogenetic tree) that are best supported by the witness quartet set. HC* extends HC in two regards: (i) whereas HC constrains the input quartet set to be unweighted (binary valued), HC* allows any positive valued quartet scores, enabling more informative quartets to be defined. (ii) HC* employs a novel collapsing technique which significantly speeds up the inference stage, making it empirically on par with quartet puzzling in terms of speed, while still guaranteeing optimal edge recovery as in HC. This paper is primarily aimed at presenting the algorithmic construction of HC*. We also report some preliminary studies on an implementation of HC* as a potentially powerful approximation scheme for maximum likelihood based inference. Details of proofs can be found in report at: ().
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".