MétaCan
Menu
Back to cohort
Record W3193057296 · doi:10.1111/tbed.14281

African swine fever risk and plant‐based feed ingredients: Canada's approach to risk management of imported feed products

2021· article· en· W3193057296 on OpenAlexaffabout
Sharon Calvin, Amy Snow, Egan Brockhoff

Bibliographic record

VenueTransboundary and Emerging Diseases · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsCanadian Poultry Research CouncilCouncil of Canadians with DisabilitiesCanadian Food Inspection Agency
Fundersnot available
KeywordsRisk managementAfrican swine feverBusinessRisk assessmentLegislatureGovernment (linguistics)Risk analysis (engineering)Environmental planningBiotechnologyEnvironmental healthEnvironmental scienceGeographyComputer scienceMedicineBiologyFinanceVirologyComputer securityVirus

Abstract

fetched live from OpenAlex

As a result of unprecedented spread of African swine fever (ASF) since 2018, Canada has taken additional steps to prevent introduction of the virus. While the role of plant-based feed in the transmission of ASF is not completely understood, it was identified that no mitigation measures were in place to address this uncertain risk. A risk analysis process was conducted with collaboration between government and industry, including an assessment of the costs and benefits of various risk mitigation options. Using existing legislative tools, requirements must now be met before the importation of plant-based feed ingredients of concern is permitted. Even with an uncertain risk, countries such as Canada that would suffer severe consequences should ASF be introduced, need to consider appropriate, risk-based mitigation measures.

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)0.008
metaresearch head score (Gemma)0.012
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: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.568

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.003
Science and technology studies0.0070.004
Scholarly communication0.0110.002
Open science0.0030.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.202
Teacher spread0.188 · 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
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

Citations9
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueTransboundary and Emerging DiseasesSame topicAnimal Disease Management and EpidemiologyFrench-language works237,207