DNA Yields and PCR Amplification Success Using Degraded Animal Corpses
Bibliographic record
Abstract
A recurring problem with ancient specimens from wildlife animals is that their preserved tissues contain small amounts of DNA in a degraded state. The specific objectives of the present study were: (1) To determine DNA yields from different animal tissues; (2) to compare traditional (manual) DNA extraction protocols with commercial procedures and (3) to assess the success of PCR amplification of Inter-Simple Sequence Repeats (ISSR) loci in degraded animal samples. Liver, stomach and muscle samples were extracted from coyote (Canis latrans) and long-tailed weasel (Mustela frenata) for this research. Manual protocols for DNA extraction were compared to a commercial kit procedure (Qiagen DNeasy kit). Genomic DNA in different states (intact, apoptotic and degraded) were amplified using a panel of ISSR primers. No DNA was recovered from coyote stomach samples using the manual extraction protocol. DNA concentrations in stomach and liver samples from coyote were 10.31 ng/μL and 15.8 ng/μL, respectively using the Qiagen extraction kit. In general, the kit extraction method yielded more DNA than the manual extraction procedure but it is more expensive. Intact and apoptotic genomic DNA were successfully amplified by PCR resulting in a similar profile. Artificially degraded DNA showed partial amplification. Thus, the ISSR marker system is suitable for animal population genetics when only limited and/or degraded animal DNA is available.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".