A parametric bootstrap approach to the detection of phylogenetic signals in landmark data
Bibliographic record
Abstract
A phylogenetic signal is present in a morphometric data set if similarities in form reflect genealogical relationships. The degree to which such a reflection exists can be measured by comparing the topology of a morphometric-based hierarchical clustering with the topology of a cladogram that is specified a priori using other sources of data. A strong phylogenetic signal is indicated by a high degree of agreement between topologies. A lack of agreement is indicative either of data with a strong “alternative” signal (attributable to homoplasy) or of data with a lack of a signal of any kind. In considering the uncertainties inherent in morphometric data, we present a new method for detecting phylogenetic signals when form is described using landmark coordinate data. We provide a parametric bootstrapping algorithm that, while applied to landmarks, is general enough to be applied to any sort of morphometric data where a reasonable model of within-sample variation can be specified. We then demonstrate how the bootstrap data can be used to make topological comparisons between morphometric clusterings and the cladogram, using: 1) bootstrap proportions attached to cladogram nodes; 2) tree-comparison statistics; and 3) analysis of the frequencies of morphometric-based clusterings that occur when bootstrapping under the model. We then demonstrate our method by examining phylogenetic patterning in midfacial shape for ateline primates. We conclude by discussing topics where more research is needed, concentrating on efforts to partition morphometric data into homologous and homoplasious components.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".