Assessing the legal, illegal, and gray ornamental trade of the critically endangered helmeted hornbill
Bibliographic record
Abstract
Monitoring wildlife trade dynamics is an important initial step for conservation action and demand reduction campaigns to reduce illegal wildlife trade. Studies often rely on one data source to assess a species' trade, such as seizures or the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) trade data. Each database provides useful information but is often incomplete. Combining information from multiple sources helps provide a more complete understanding of trade. A recent rapid increase in demand for helmeted hornbill (Rhinoplax vigil) casques (a brightly colored, solid keratinous rostrum) led to its uplisting to critically endangered on the International Union for Conservation of Nature Red List in 2015. However, there is little current information on what factors influence trade trends and what current levels of demand are. We combined data from CITES, seizure records, and previously underused, yet abundant, art and antique auction data to examine the global trade in helmeted hornbill casque products (HHPs). Three decades of auction data revealed that 1027 individual HHPs had been auctioned since 1992; total auction sales were over US$3 million from 1992 to 2021. The number of HHPs auctioned was greatest from 2011 to 2014, just after the global art boom (2009-2011), followed by a decline in volume and price. The auction data also revealed 2 possible markets for HHPs: true antique and speculative, defined by era, price, and trade patterns. Trends in illegal trade matched those of the auction market, but legal trade remained consistently low. Combining data sources from legal, illegal, and gray markets provided an overview of the dynamics of illegal trade in an endangered species. This approach can be applied to other wildlife markets to provide a more complete understanding of trade and demand at the market level to inform future demand reduction campaigns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".