Construction And Verification Of A Large Phylogeny Of Marsupials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it