Bibliographic record
Abstract
In general when a phylogeny is reconstructed from DNA or protein sequence data, it makes use only of the probabilities of obtaining some phylogeny given a collection of data. It is also possible to determine the prior probabilities of different phylogenies. This information can be of use in analyzing the biological causes for the observed divergence of sampled taxa. Unusually "rare" topologies for a given data set may be indicative of different biological forces acting. A recursive algorithm is presented that calculates the prior probabilities of a phylogeny for different allelic samples and for different phylogenies. This method is a straightforward extension of Ewens' sample distribution. The probability of obtaining each possible sample according to Ewens' distribution is further subdivided into each of the possible phylogenetic topologies. These probabilities depend not only on the identity of the alleles and on 4N(mu) (four times the effective population size times the neutral mutation rate) but also on the phylogenetic relationships among the alleles. Illustrations of the algorithm are given to demonstrate how different phylogenies are favored under different conditions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".