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Record W4289714340 · doi:10.20506/rst.41.1.3317

Issues and gaps in international guidance and national regulatory systems affecting international live insect trade

2022· review· en· W4289714340 on OpenAlexaff
M.M. QUINLAN, John Mumford, S. MESSORI, W.R. ENKERLIN, J. SHIMURA, Laura Madeleine Smith, Brinda Dass, C.F. OLIVA, C. NELSON, R. CHAND, G. TORRES

Bibliographic record

VenueRevue Scientifique et Technique de l OIE · 2022
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Utilization and Effects
Canadian institutionsUniversity of Guelph
FundersEuropean CommissionImperial College LondonOpen Philanthropy ProjectBill and Melinda Gates Foundation
KeywordsVariety (cybernetics)BusinessDiversity (politics)International tradeEnvironmental resource managementEnvironmental planningEconomicsPolitical scienceGeographyComputer science

Abstract

fetched live from OpenAlex

International trade in live insects involves the shipping of many different species, for various purposes, with a variety of handling requirements regulated by numerous authorities with varying objectives. The diversity of factors at play has both created and been subject to a complex regulatory landscape. A review of global production, shipping and use experiences from a range of perspectives has shown gaps and inconsistencies in international guidance and national implementation. Private carriers add another layer of uncertainty that is disproportionate to risks, resulting in variable practices and charges. Many benefits can come from international trade in insects, including pollinator services, control of pests and of disease vectors, and enhanced international scientific research and innovation. These benefits will be better achieved through a more evidence-based and efficient approach to regulating trade. This change in approach will in turn require an improved and widely accepted risk-management landscape for insect trade.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.005
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.061
GPT teacher head0.314
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations6
Published2022
Admission routes1
Has abstractyes

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Same venueRevue Scientifique et Technique de l OIESame topicInsect Utilization and EffectsFrench-language works237,207