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Record W3182259186 · doi:10.3389/ffgc.2021.700825

Forest Biosecurity in Canada – An Integrated Multi-Agency Approach

2021· article· en· W3182259186 on OpenAlexafffundabout
Jeremy D. Allison, Mireille Marcotte, Meghan K. Noseworthy, Tod D. Ramsfield

Bibliographic record

VenueFrontiers in Forests and Global Change · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCanadian Food Inspection AgencyCanadian Forest Service
FundersNatural Resources CanadaU.S. Forest ServiceAustralian GovernmentCanadian Forest ServiceCanadian Food Inspection Agency
KeywordsBiosecurityAgency (philosophy)MandateGovernment (linguistics)BusinessJurisdictionEnvironmental planningCertified woodConventionEnvironmental protectionEnvironmental resource managementPublic administrationForestryForest managementPolitical scienceGeographyEcology

Abstract

fetched live from OpenAlex

In Canada, forest biosecurity is primarily under federal jurisdiction as the federal government is the signatory to the International Plant Protection Convention and other international trade agreements. The Canadian Food Inspection Agency (CFIA), which is Canada’s National Plant Protection Organization, has the mandate of analyzing risks, setting policy, and managing incursion responses related to forest biosecurity. Other federal government agencies play important roles; the Canada Border Services Agency (CBSA) enforces regulations at international ports of entry and the Canadian Forest Service of Natural Resources Canada conducts research and analysis in support of the development and implementation of phytosanitary regulations. The provinces and territories also manage invasive species through implementing regulations to prevent the spread of established forest pests. This paper outlines the regulatory framework for forest biosecurity within Canada, and provides case studies of species that have invaded Canadian forests or are anticipated to do so in the near future.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.220
Teacher spread0.202 · 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 designObservational
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

Citations18
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueFrontiers in Forests and Global ChangeSame topicForest Insect Ecology and ManagementFrench-language works237,207