Rooting Phylogenetic Trees with Distant Outgroups: A Case Study from the Commelinoid Monocots
Bibliographic record
Abstract
Phylogenetic rooting experiments demonstrate that two chloroplast genes from commelinoid monocot taxa that represent the closest living relatives of the pickerelweed family, Pontederiaceae, retain measurable signals regarding the position of that family's root. The rooting preferences of the chloroplast sequences were compared with those for artificial sequences that correspond to outgroups so divergent that their signal has been lost completely. These random sequences prefer the three longest branches in the unrooted ingroup topology and do not preferentially root on the branches favored by real outgroup sequences. However, the rooting behavior of the artificial sequences is not a simple function of branch length. The random outgroups preferentially root on long terminal ingroup branches, but many ingroup branches comparable in length to those favored by random sequences attract no or few hits. Nonterminal ingroup branches are generally avoided, regardless of their length. Comparisons of the ease of forcing sequences onto suboptimal roots indicate that real outgroups require a substantially greater rooting penalty than random outgroups for around half of the least-parsimonious candidate roots. Although this supports the existence of nonrandomized signal in the real outgroups, it also indicates that there is little power to choose among the optimal and nearly optimal rooting possibilities. A likelihood-based test rejects the hypothesis that all rootings of the subtree using real outgroup sequences are equally good explanations of the data and also eliminates around half of the least optimal candidate roots. Adding genes or outgroups can improve the ability to discriminate among different root locations. Rooting discriminatory power is shown to be stronger, in general, for more closely related outgroups and is highly correlated among different real outgroups, genes, and optimality criteria.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".