Profiling Volatilomes: A Novel Forensic Method for Identification of Confiscated Illegal Wildlife Items
Bibliographic record
Abstract
Globally, the rapid decline in wildlife species has many causes. The illegal trafficking of fauna and flora is a major contributor to species decline and continues to grow at an alarming rate. To enable the prosecution of those involved in the trafficking of illegal wildlife, accurate and reliable identification is paramount. Traditionally, morphology and DNA amplification are used. This paper investigates a novel application of volatilome profiling using comprehensive two-dimensional gas chromatography coupled with time of flight mass spectrometry for wildlife sample detection. Known samples of elephant-derived ivory, other dentine samples, and bone (a common ivory substitute) were used as reference samples for volatilome profiling. Subsequently, specimens that were suspected ivory from border control seizures were obtained and analysed. Confirmatory DNA analyses were conducted on seized samples to establish the reliability parameters of volatilome profiling. The volatilome method correctly identified six of the eight seized samples as elephant ivory, which was confirmed through DNA analysis. There was also clear distinction of African elephant ivory parts from the bone and dentine samples from other species, as shown through PCA and discriminant analyses. These preliminary results establish volatilome profiling through GC×GC-TOFMS as a novel screening method used for the identification of unknown wildlife contraband.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".