A high throughput ambient mass spectrometric approach for identifying the poaching of wild american ginseng
Bibliographic record
Abstract
Abstract Rapid identification is critically important in the protection of endangered species listed under the Convention on International Trade in Endangered Species (CITES). One such species is American ginseng ( Panax quinquefolius ), whose remaining wild populations are vulnerable to the effects of poaching. Direct Analysis in Real Time Time-of-Flight Mass Spectrometry (DART-ToF MS) is a mature but underutilized forensic tool suitable for rapidly analyzing plant materials. This tool offers greater convenience over alternative species identification methods commonly requiring extensive sample preparation and instrument run times. In the current study, four categories of ginseng, including wild and cultivated American ginseng, Korean ginseng ( P. ginseng ), and Chinese ginseng ( P. notoginseng ), were analyzed by DART-ToF MS. The collected mass spectra were visually compared by heat map prior to application of multivariate statistical analysis to cluster sample groups, yielding a two-step identification model capable of identifying the origin of blind quality assurance samples. With fast sample preparation, data acquisition, and statistical analysis, DART-ToF MS shows great potential as a forensic screening tool in combating poaching and illegal trade of endangered and CITES-listed species such as wild American ginseng.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".