Micro-CT X-rays do not fragment DNA in preserved bird skins
Bibliographic record
Abstract
Most zoological systematics studies are currently based on morphological features, molecular traits or a combination of both to reconstruct animals’ phylogenetic history. Increasingly, morphological studies of museum specimens are using X-ray computed tomography to visualize internal morphology, because of its ‘non-destructive’ nature. However, it is not known whether CT can fragment the size of DNA extracted from museum specimens, as has been demonstrated to occur in living cells. This question is of paramount importance for collections based research because X-rays may reduce the amount of data obtainable from specimens. In our study, we tested whether exposure of museum bird skins to typical CT X-ray energies (for visualization of the skeleton) increased DNA strand fragmentation, a key factor for the success of downstream molecular applications. For the present study, we extracted DNA from shavings of 24 prepared and dried bird skins (100+ years) footpads before and after CT scanning. The pre- and post-CT fragmentation profiles were assessed using a capillary electrophoresis high-precision instrument (Agilent Bioanalyzer). Comparison of the most common strand length in each DNA sample (relative mass) revealed no significant difference unexposed and exposed tissue (paired t-test p = 0.463). In conclusion, we found no further quantifiable degradation of DNA strand length under standard X-ray exposure obtained from our bird skins sample. Differences in museum preservation techniques probably had a greater effect on variation of pre-CT DNA fragmentation.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".