The bear necessities: A sensitive qPCR assay for bear DNA detection from bile and derived products to complement wildlife forensic enforcement
Bibliographic record
Abstract
Demand for bear bile, a prized component of traditional Asian medicines, threaten Asiatic and sun bear population sustainability. While laws exist to prevent poaching and trafficking of bear parts and derivatives, smuggling persists with demand extending to surrogate species, including American black bears (Ursus americanus). Mitochondrial DNA (mtDNA) sequencing can identify products putatively containing biological bear material but can be undermined by PCR inhibitors in bile and a lack of sensitivity at trace levels. Quantitative PCR (qPCR) assays can be used to distinguish between closely related target species, while concomitantly evaluating inhibition and false negative results in low quality/quantity DNA applications. Herein, we develop a multiplexed qPCR assay to detect and differentiate among bear species, including highly diluted bile samples mixed within liquors as common dilutants. The assay detects as little as 10 locus copies/reaction of bear DNA with 95% confidence, distinguishing among sun, Asiatic and American black bears. Demonstrating the sensitivity and applicability of this assay in context of current bile mixture recipes, dilutions of 1:5,000 bile with ethanol, red wine, and spirits, all yielded clear quantifiable detections, where our data suggests as little as 1 drop of bile per 750 mL bottle of alcohol would still exceed the limits of detection (e.g., 1:15000 dilution or <0.05 mL bile per 750 mL bottle). Overall, this study provides a rapid, sensitive, and specific test to identify and distinguish among bear species commonly used for bile production to aid wildlife enforcement applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".