Evolutionary history of the Galápagos Rail revealed by ancient mitogenomes and modern samples
Bibliographic record
Abstract
Beast v. 2.6.3 input (.xml) files and output (.log and .trees) files for phylogenetic analyses of rails, used to determined the evolutionary history of the Galápagos Rail Laterallus spilonota. There are two main datasets: coding sequences of the mitochondrial genome ('mtCDS'), partitioned per codon position, and a two mitochondrial/one nuclear marker dataset ('2mt1nc'). For each of the datasets, separate runs have been made in which the fossil calibration of Rallidae is applied to the stem of the present-day family ('calRallidaeStem') or the crown node ('calRallidaeCrown), and finally all runs have been replicated with three different starting seeds ('seed_NNNNNNNNN', with the different seeds 123456789, 456789123, and 789123456). We provide raw output (.log and .raw.trees) as well as maximum clade credibility ('mcc') trees (.mcc.trees), calculated after discarding 10% of the trees as burn-in, using median ('heights_median') or mean ('heights_mean') node heights as estimated node age. The runs used for Table 1 (and Figure 2) in the accompanying paper are: Dataset mtCDS, Rallidae calibration of stem: seed 123456789 Dataset mtCDS, Rallidae calibration of crown: seed 456789123 Dataset 2mt1nc, Rallidae calibration of stem: seed 789123456 Dataset 2mt1nc, Rallidae calibration of crown: seed 123456789 This version of the data includes Pellornis mikkelseni among the fossils making up the calibration distribution for crown Gruiformes. In a previous version of this data deposit, that data point was represented by Messelornis cristata (see accompanying paper).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".