Predicting sample success for large‐scale ancient DNA studies on marine mammals
Bibliographic record
Abstract
In recent years, nonhuman ancient DNA studies have begun to focus on larger sample sizes and whole genomes, offering the potential to reveal exciting and hitherto unknown answers to ongoing biological and archaeological questions. However, one major limitation to such studies is the substantial financial and time investments still required during sample screening, due to uncertainty regarding successful sample selection. This study investigates the effect of a wide range of sample properties including latitude, sample age, skeletal element, collagen preservation, and context on endogenous content and DNA damage profiles for 317 ancient and historic pinniped samples collected from across the North Atlantic and surrounding regions. Using generalised linear and mixed-effect models, we found that a range of factors affected DNA preservation within each of the species under consideration. The most important findings were that endogenous content varied significantly within species according to context, the type of skeletal element, the collagen content and collection year. There also appears to be an effect of the sample's geographic origin, with samples from the Arctic generally showing higher endogenous content and lower damage rates. Both latitude and sample age were found to have significant relationships with damage levels, but only for walrus samples. Sex, ontogenetic age and extraction material preparation were not found to have any significant relationship with DNA preservation. Overall, skeletal element and sample context were found to be the most influential factors and should therefore be considered when selecting samples for large-scale ancient genome studies.
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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 | 0.027 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".