Not only better sampling, but also better modelling
Bibliographic record
Abstract
Most, if not all, of the sites in a sequence do not evolve according to the same pattern; this is because each residue is characterised by specific biophysical environments and different evolutionary constraints. Homogenous models of sequence evolution fail to account for this assumption and may, for example, misinterpret the non-phylogenetic signal embedded in highly saturated positions; indeed, the deeper the nodes in the phylogeny, the higher the risk of falling in such type of systematic error. An effecting way of overcoming this problem is the employment of among-site heterogeneous models of sequence evolution. Here we outline various examples of how these models, most of which belongs to the “CAT-family”, have helped producing phylogenies that significantly differs to those obtained using homogenous models. Examples span from arthropods to rodents, and from large phylogenomic to mitogenomic and classical rRNA datasets. Not only heterogeneous models are clear improvement in term of fit to the datasets, but notably recover phylogenies more congruent with morphology and other sources of evidence. We also show that the use of among-site heterogeneous models also affects molecular clock estimates, which are typically older when using homogeneous models in nodes describing radiations of fast evolving species. Although both an adequate taxon and gene sampling are needed to address many phylogenetic problems, we advocate that more importance should be given to the accurate modelling of sequences rather than to massive harvesting of data, except if this allows to break long branches
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".