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Record W4393043906 · doi:10.1111/epp.12989

Including climate change in pest risk assessment: Current practices and perspectives for future implementation

2024· article· en· W4393043906 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEPPO Bulletin · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCanadian Food Inspection Agency
Fundersnot available
KeywordsCurrent (fluid)Climate changePEST analysisEnvironmental planningEnvironmental resource managementRisk analysis (engineering)Environmental scienceBusinessEcologyEngineeringBiology

Abstract

fetched live from OpenAlex

Abstract The evaluation of the potential for newly arrived species to survive and the determination whether a founder population can become established and subsequently spread and cause negative impacts are crucial considerations when performing a pest risk assessment in plant health. Climate change has clear consequences concerning the potential range of pests, and their potential for spread and impacts. Despite its importance, no guidance exists to support the evaluation of whether and how climate change should be incorporated into pest risk assessment. This paper reviews how climate change has been considered so far, not only in the area of pest risk assessment but also in other domains and provides guidance on how its incorporation could affect the overall assessment. Furthermore, from this analysis, some possible solutions for incorporating climate change into pest risk assessment are provided, taking into account that its outcomes have profound political, economic, social and environmental implications.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.040
GPT teacher head0.371
Teacher spread0.332 · 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