Beyond the present: How climate change is relevant to pest risk analysis
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.
Bibliographic record
Abstract
Abstract Climate change is widely recognized as a critical global challenge with far‐reaching consequences. It affects pest species by altering their population dynamics, actual and potential distribution areas, as well as interactions with their hosts and natural enemies. Climate change thus has potentially important implications for multiple areas of the pest risk analysis (PRA) process. The importance of including climate change in PRA may vary depending on the climatic context of the PRA area in relation to the speed of climate change. If climatic changes within the time horizon of interest are minimal, their potential impact on pest risk is reduced accordingly. For PRAs in a changing climate, we need to be concerned with how future climates could alter our assessment of the risks currently posed by each pest species. While climate can influence the distribution and abundance of pests and hosts alike, its significance will vary depending on the situation. The inclusion of climate change within a PRA also presents challenges. The dynamic nature of climate change, with its complex interactions and uncertainties, can make it difficult to predict and assess the future risks posed by pests accurately. Uncertainties related to future predictions may be much greater than the potential effects associated with climate change and species’ responses to it. This paper outlines examples of the effects of climate change on hosts and different groups of pests, including invertebrates, pathogens, weeds and vector species. The aim is to review the opportunities and challenges of incorporating climate change into PRA, offering insights for a variety of stakeholders including policymakers on this topic.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.016 |
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 it