Isotopic applications assit in forensic tracking of illegally traded wildlife parts
Bibliographic record
Abstract
Isotopic applications assit in forensic tracking of illegally traded wildlife parts Keith A. Hobson, a Research Scientist and Professor at Environment and Climate Change Canada, discusses the use of stable isotopes to trace the origins of animal parts in order to mitigate the illegal wildlife trade. As of 2022, the illegal global trade in tissues of (CITES and non-CITES listed) wildlife has been estimated to be on the order of $220 billion,(1) placing this practice among the top four of all global criminal enterprises. As ecosystems and the wild animals and plants they harbor come under increasing pressure from human developments, such trade threatens many species with decimation and ultimate extinction. Governments continue to struggle with the extent of this phenomenon and generally have few tools available to counter this growing trend. However, once seized, wildlife parts can be examined forensically to help ascertain provenance, and such tools can contribute in a small way to counter such criminal activity.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".