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Record W3093504035 · doi:10.1007/s10344-020-01425-7

Bear trade in the Czech Republic: an analysis of legal and illegal international trade from 2005 to 2020

2020· article· en· W3093504035 on OpenAlexaffabout
Chris R. Shepherd, Jitka Kufnerová, Tomáš Cajthaml, Jaroslava Frouzová, Lalita Gomez

Bibliographic record

VenueEuropean Journal of Wildlife Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIdentification and Quantification in Food
Canadian institutionsWildlife Conservation Society Canada
Fundersnot available
KeywordsCITESCzechChinaWildlife tradeInternational tradeGeographyEndangered speciesConventionPolitical scienceBusinessWildlifeLawEcologyEnvironmental healthArchaeologyBiologyMedicinePopulation

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.357
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2020
Admission routes2
Has abstractyes

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