Evolutionary history of the Galápagos Rail revealed by ancient mitogenomes and modern samples
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
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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