Including climate change in pest risk assessment: Current practices and perspectives for future implementation
Bibliographic record
Abstract
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.
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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 | 0.074 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".