Phylogenetics of extant and fossil Pinaceae: methods for increasing topological stability
Bibliographic record
Abstract
Relationships of living and fossil Pinaceae were inferred using parsimony and Bayesian inference of morphological characters and plastid and nuclear DNA sequences. When considering extant taxa only, adding molecular to morphological characters resulted in markedly increased resolution and branch support compared with analysis of morphology alone. Including 45 fossil taxa resulted in drastically decreased resolution in morphology-based consensus trees. We evaluated the effect on branch support and resolution of including DNA sequences, deleting fossils lacking information for cone scale apices and seeds, using reduced consensus methods, and using implied weighting, and found that the greatest improvements were found by including DNA sequences and using implied weighting. The tree topologies from parsimony and Bayesian inference confirm previous findings that the fossil genus Pseudoaraucaria and a few species of Pityostrobus from the Lower Cretaceous are related to abietoid genera, and that other species of Pityostrobus are pinoid and closely related to Pinus. Focusing phylogenetic analyses on the most complete fossil cones, specifically those that are anatomically preserved and include both cone scale apices and seeds, and taking into account homoplasy, resulted in the clearest hypotheses for the timing and sequence of diversification in the family.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".