The Phylogenetic Trunk: Maximal Inclusion of Taxa with Missing Data in an Analysis of the Lepospondyli (Vertebrata, Tetrapoda)
Bibliographic record
Abstract
The importance of fossils to phylogenetic reconstruction is well established. However, analyses of fossil data sets are confounded by problems related to the less complete nature of the specimens. Taxa that are incompletely known are problematic because of the uncertainty of their placement within a tree, leading to a proliferation of most-parsimonious solutions and "wild card" behavior. Problematic taxa are commonly deleted based on a priori criteria of completeness. Paradoxically, a taxon's problematic behavior is tree dependent, and levels of completeness are not directly associated with problematic behavior. Exclusion of taxa on the basis of completeness eliminates real character conflict and, by not allowing incomplete taxa to determine tree topology, diminishes the phylogenetic hypothesis. Here, the phylogenetic trunk approach is proposed to allow optimization of taxonomic inclusion and tree stability. The use of this method in an analysis of the Paleozoic Lepospondyli finds a single most-parsimonious tree, or trunk, after the removal of one taxon identified as being problematic. Moreover, the 38 trees found at one additional step from this primary trunk were reduced to 2 by removal of one additional taxon. These trunks are compared with the trees that were found by excluding taxa with various degrees of completeness, and the effects of incomplete taxa are explored with regard to use of the trunk. Correlated characters associated with limblessness are discussed regarding the assumption of character independence; however, inclusion of intermediate taxa is found to be the single best method for breaking down long branches.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".