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Prosecution records reveal pangolin trading networks in China, 2014–2019

2021· letter· en· W3198083168 on OpenAlexaff
Xiang-Qin Huang, Chris Newman, Christina D. Buesching, Mei-Ling Shao, Yun‐Chun Ye, Sha Liu, David W. Macdonald, Zhao‐Min Zhou

Bibliographic record

Venue动物学研究 · 2021
Typeletter
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsOkanagan University CollegeNatural Resources Canada
Fundersnot available
KeywordsPangolinWildlife tradeChinaGeographyContext (archaeology)LivestockLegislationWildlifeOutbreakBusinessInternational tradeFisherySocioeconomicsEcologyBiologyPolitical scienceForestryEconomics

Abstract

fetched live from OpenAlex

In a precautionary response to the current coronavirus (COVID-19) pandemic, China's Ministries permanently banned eating and trading in terrestrial wild (non-livestock) animals on 24 February 2020, and extensively updated the list of Fauna under Special State Protection (LFSSP) in 2020 and 2021, in which pangolins (Manidae spp.) were upgraded to the highest protection level. Examining 509 pangolin prosecution records from China Judgements online prior to these changes (01/01/14-31/12/19), we identified that Guangdong, Guangxi and Yunnan Provinces were hotspots for trade in whole pangolins and their scales. Interrupting trade in these three principal southern provinces would substantially fragment the pangolin trade network and reduce supply of imports from other south-east Asian countries. In the context of the revised legislation and strategies intended to prevent wildlife trade, we conclude that targeting interventions at key trade nodes could significantly reduce illegal trade in pangolins, and that this approach could also be effective with other taxa.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.278
Teacher spread0.260 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations13
Published2021
Admission routes1
Has abstractyes

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