Sex identification of ancient pinnipeds using the dog genome
Bibliographic record
Abstract
Abstract Determining the male and female representation in zooarchaeological material from hunted animal species is essential, to fully investigate the effects and means of prehistoric hunting practices, and may further provide valuable biological information on past animal life-history, behaviour and demography. However, the fragmented nature of the zooarchaeological record and a lack of clear diagnostic skeletal markers, often prevents such inference. Here, we test the usability of the dog nuclear genome (CanFam3.1) for sex identification of pinnipeds. First, a contemporary sample set (n=72) of ringed seals ( Pusa hispida ), with known sex was used to test the genetic sex identification method. By quantifying the proportion of X chromosome reads, as the chrX/chr1 ratio, the ratios clustered in two clearly distinguishable sex groups. Of the 72 individuals, 69 were identified to the accurate sex, which proves a high reliability of the genetic method. Second, random down sampling of a subset of the ringed seal samples to different read number, suggests at least 5000 DNA sequence reads mapped to the reference genome as the lower limit for which this method is applicable. Finally, applying this standard, sex identification was successfully carried out on a broad set of ancient pinniped samples, including walruses ( Odobenus rosmarus ), grey seals ( Halichoerus grypus ) and harp seals ( Pagophilus groenlandicus) , which all showed clearly distinct male and female chrX/chr1 ratio groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".