Construction And Verification Of A Large Phylogeny Of Marsupials
Bibliographic record
Abstract
Much of the controversy over marsupial phylogeny at higher-categorical levels stems from the piecemeal nature of the contributing studies or the paucity of taxonomic representation in many of them. Yet the problems of constructing large phylogenies are manyfold, involving the initial generation of the data as well as their efficient analysis. Often unaddressed, also, is the need to validate extremely large data sets and trees. Many of these problems can be ameliorated by treating the data as distances (or generating distances directly). We show that, contrary to the assertions of many protagonists in the total-evidence versus consensus debate, the validated data and pathlength (tree) matrices usually give very similar results, although a few additional unstable nodes may be found when the results of internal and external validations are themselves combined in a global-congruence test. Here we illustrate our protocols with a 109-taxon data set, representing combination of marsupial DNA-hybridisation data with similar information on a series of outgroups. Phylogenetically, the results affirm the marsupial groupings we have previously found, and suggest but do not unambiguously support a nearer relationship of monotremes than placentals to marsupials. This paper represents the first attempt to validate the tree of 101 marsupials presented earlier.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".