Extending phylogenetic studies of coevolution: secondary Brooks parsimony analysis, parasites, and the Great Apes
Bibliographic record
Abstract
Abstract Dowling recently compared the empirical properties of Brooks parsimony analysis (BPA) and the leading method for studying phylogenetic aspects of coevolution, reconciled tree analysis (using the computer program TreeMap), based on a series of simulations. Like the majority of authors who have compared BPA with other methods, however, Dowling considered only the form of BPA proposed in 1981 and did not take into account various modifications of the method proposed from 1986 to 2002. This leaves some doubt as to the robustness of his assessments of both the superiority of BPA and its shortcomings. We provide a précis of the principles of contemporary BPA, including ways to implement it algorithmically, using either Wagner algorithm‐based or Hennigian argumentation‐based approaches, followed by an empirical example. Our study supports Dowling's fundamental conclusions about the superiority of primary BPA relative to TreeMap. However, his conclusions about the shortcomings of BPA due to inclusive ORing (i.e., the production of ghost taxa) are incorrect, as secondary BPA eliminates inclusive ORing from the method. Secondary BPA provides a more complete account of the evolutionary associations between the parasite groups and their hosts than does primary BPA, without sacrificing any indirectly generated information about host phylogeny. Secondary BPA of two groups of nematodes inhabiting Great Apes shows that TreeMap analysis underestimated the amount of cospeciation in the evolution of the nematode genus Enterobius.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".