Bear trade in the Czech Republic: an analysis of legal and illegal international trade from 2005 to 2020
Bibliographic record
Abstract
Abstract There is a large demand for bear parts in the Czech Republic, and this drives legal and illegal trade in various bear species sourced from outside the country. From 2010 to 2018, the Czech Republic reported legal imports of 495 bear parts, mostly as trophies from Canada and Russia. Illegal trade in bear parts and derivatives for medicine as well as trophies persists as evidenced by the number of seizures made by the Czech Environmental Inspectorate during this same period. From January 2005 to February 2020, 36 seizures involving bears, their parts and derivatives, were made totalling 346 items. Most cases involved trophies (skins, skulls, taxidermies) predominantly from Canada, Russia and the USA, followed by traditional medicines claiming to contain bear parts mostly from Vietnam and China. Three cases involved souvenirs or jewellery, and one case involved live bear cubs. The greatest number of seizures made originated from Vietnam, followed by Canada and Russia. As all countries involved in these incidents are Parties to the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), there is a mechanism in place to jointly tackle this illegal trade. International collaboration is essential if efforts to end the illegal international trade in bear parts and derivatives are to succeed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".