Exploring DNA degradation in situ and in museum storage through genomics and metagenomics
Bibliographic record
Abstract
Understanding the environmental and microbial processes involved in DNA degradation from archaeological remains is a fundamental part of managing bone specimens. We investigated the state of DNA preservation in 33 archaeozoological caribou (Rangifer tarandus) ribs excavated from the same excavation trench at a former Inuit hunting camp in West Greenland, separated by 43 years: 1978 and 2021. Our findings show that DNA is better preserved in the most recently excavated samples, indicating a detrimental effect of museum storage on DNA integrity. Additionally, our data reveals a diverse microbiome in these bones, encoding genes relevant for bone degradation, such as enzymatic families relating to collagenases, peptidases and glycosidases. Microbes associated with bone degradation were present in both new and historical samples, with museum-stored bones showing significantly more DNA damage. Overall, our research sheds light on the nuanced dynamics governing the preservation of genomic material in archaeological contexts, underscoring the vital importance of careful considerations in museum curation practices for the sustainable conservation of invaluable skeletal records in museum repositories and in situ. DNA preservation in archaeozoological caribou ribs from West Greenland Inuit hunting camp shows better quality in newly excavated samples, revealing detrimental effects of museum storage and diverse microbial communities involved in bone degradation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".