Molecular dating and diversification of the South American lizard genus Liolaemus (subgenus Eulaemus) based on nuclear and mitochondrial DNA sequences
Bibliographic record
Abstract
The temperate South American lizard genus Liolaemus is the one of the most widely distributed and species-rich genera of lizards on earth. The genus is divided into two subgenera, Liolaemus sensu stricto (the ‘Chilean group’) and Eulaemus (the ‘Argentino group’), a division that is supported by recent molecular and morphological data. Owing to a lack of reliable fossil data, previous studies have been forced to use either global molecular clocks, a standardized mutation rate adopted from previous studies, or the use of geological events as calibration points. However, simulations indicate that these types of assumptions may result in less accurate estimates of divergence times when clock-like models or mutation rates are violated. We used a multilocus data set combined with a newly described fossil to provide the first calibrated phylogeny for the crown groups of the clade Eulaemus, and derive new fossil-calibrated substitution rates (with error) of both nuclear and mtDNA gene regions for Eulaemus specifically. Divergence date estimates for each of the crown groups and appropriate rate estimates will provide the foundation for understanding rates of speciation, historical biogeography, and phylogeographical history for various clades in one of the most diverse lizard genera in the poorly studied Patagonian region.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".