ArticleScientific reports2018
Novel detection of provenance in the illegal wildlife trade using elemental data.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Detect and Trace: An Australian Field Trial Using Machine-Learning Tools to Combat Illegal Wildlife Trade.Animals : an open access journal from MDPI · 2026Article
- Wildlife trade investigations benefit from multivariate stable isotope analyses.Biological reviews of the Cambridge Philosophical Society · 2025Review
- Combined genetic and chemical methods boost the precision of tracing illegal timber in Central Africa.Communications earth & environment · 2025Article
- Deep learning-based image classification of turtles imported into Korea.Scientific reports · 2023Article
- Determining the Provenance of Traded Wildlife in the Philippines.Animals : an open access journal from MDPI · 2023Article
- Review
- Image Classification of Amazon Parrots by Deep Learning: A Potentially Useful Tool for Wildlife Conservation.Biology · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite being the fourth largest criminal market in the world, no forensic tools have been sufficiently developed to accurately determine the legal status of seized animals and their parts. Although legal trading is permissible for farmed or captive-bred animals, many animals are illegally removed from the wild and laundered by masquerading them as captive bred. Here we present high-resolution x-ray fluorescence (XRF) as a non-invasive and cost-effective tool for forensic classification. We tested the efficacy of this technique by using machine learning on a training set of zoo specimens and wild-caught individuals of short-beaked echidnas (Tachyglossus aculeatus), a small insectivorous monotreme in Australia. XRF outperformed stable isotope analysis (δ
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Registered trials
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