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New protocols to assess the environmental impact of pests in the EPPO decision‐support scheme for pest risk analysis*

2012· article· en· W2166799829 on OpenAlexaff
Marc Kenis, Sven Bacher, Richard Baker, Étienne Branquart, Sophie Brunel‐Muguet, J. Holt, Philip E. Hulme, Alan MacLeod, Jan Pergl, Françoise Petter, Petr Pyšek, Gritta Schrader, Andrea Sissons, Uwe Starfinger, Urs Schaffner

Bibliographic record

VenueEPPO Bulletin · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCanadian Food Inspection Agency
FundersBundesamt für UmweltEnergy Policy and Planning OfficeSeventh Framework ProgrammeAkademie Věd České Republiky
KeywordsRisk assessmentPEST analysisConsistency (knowledge bases)Risk analysis (engineering)Environmental impact assessmentEnvironmental risk assessmentEnvironmental planningAlienEnvironmental resource managementBusinessComputer scienceEnvironmental scienceEcologyEnvironmental healthBiologyMedicineComputer security

Abstract

fetched live from OpenAlex

Assessing the potential environmental impact of alien plants and plant pests is notoriously difficult. New protocols have been developed in the framework of the EC project PRATIQUE to provide guidance on environmental impact assessment in the EPPO pest risk analysis (PRA) decision‐support scheme and enhance consistency between risk assessors and risk ratings for different pests. A set of questions with rating guidance and examples is provided, and individual scores are summarized into final scores, using a hierarchy of risk matrices, to assess current and potential environmental impacts. Two separate protocols are available: for alien plants and for other pests. These protocols could also be used to assess environmental impact in other PRA schemes as well as to assign alien species to regional black lists or to prioritize species for management decisions.

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)0.055
metaresearch head score (Gemma)0.070
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: Protocol · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.289

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.070
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0030.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0370.014

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 head0.022
GPT teacher head0.313
Teacher spread0.292 · 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
GenreProtocol

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

Citations42
Published2012
Admission routes1
Has abstractyes

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