Proposal of a ranking methodology for plant threats in the EU
Bibliographic record
Abstract
Following a request of the European Commission, EFSA and ANSES, beneficiary of the EFSA tasking grant on horizon scanning for plant pests (GP/EFSA/ALPHA/2017/02), developed a methodology to order by risk non-regulated pests recently identified through the monitoring of media and scientific literature. The ranking methodology proposed at the end of the pilot phase was based on the scoring of pests under evaluation following 16 criteria related to the steps of the pest risk assessment scheme. The multicriteria matrix of scores obtained was then submitted to the multicriteria analysis method PROMETHEE. The pilot methodology was tested on a limited number of pests (14 pests identified during the monitoring activity, and 4 'control' pests whose well-known risk should be reflected either in a positive or negative score), then applied on all non-regulated pests identified through the media and scientific literature monitoring in the first 2 years of the project. After having collected feedback from the targeted final users (EU risk managers), the methodology underwent a few refinements: (i) implementation of the methodology to a set of already assessed reference pests from EFSA opinions, (ii) exclusions of three criteria from the scoring phase, (iii) identification of pests proposed for further action ('positive' pests), using a threshold defined after scoring the reference pests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".