Consistent divergence times and allele sharing measured from cross‐species application of <scp>SNP</scp> chips developed for three domestic species
Bibliographic record
Abstract
Recent advances in technology facilitated development of large sets of genetic markers for many taxa, though most often model or domestic organisms. Cross-species application of genomic technologies may allow for rapid marker discovery in wild relatives of taxa with well-developed resources. We investigated returns from cross-species application of three commercially available SNP chips (the OvineSNP50, BovineSNP50 and EquineSNP50 BeadChips) as a function of divergence time between the domestic source species and wild target species. Across all three chips, we observed a consistent linear decrease in call rate (~1.5% per million years), while retention of polymorphisms showed an exponential decay. These results will allow researchers to predict the expected amplification rate and polymorphism of cross-species application for their taxa of interest, as well as provide a resource for estimating divergence times.
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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".