A real-time PCR assay to accurately quantify polar bear DNA in fecal extracts
Bibliographic record
Abstract
DNA extracted from fecal samples contains DNA from the focal species, food, bacteria and pathogens. Most DNA quantification methods measure total DNA and cannot differentiate among sources. Despite the desirability of noninvasive fecal sampling for studying wildlife populations, low amounts of focal species DNA make it difficult to use for next-generation sequencing (NGS), where accurate DNA quantification is critical for normalization. Two factors are required prior to using fecal samples in NGS libraries: (1) an accurate quantification method for the amount of target DNA and (2) a determination of the relative amount of target DNA needed for successful single nucleotide polymorphism genotyping assays. Here, we address these needs by developing primers to amplify a 101 bp region of the nuclear F2 gene and a quantitative PCR (qPCR) assay that allows the accurate quantification of the amount of polar bear (Ursus maritimus) DNA in fecal extracts. We test the assay on pure polar bear DNA extracted from muscle tissue and find a high correlation between fluorometric and qPCR quantifications. The qPCR assay was also successfully used to quantify the amount of DNA derived from polar bears in fecal extractions. Orthologs of the F2 gene have been identified across vertebrates; thus, similar qPCR assays could be developed for other species to enable noninvasive 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".