SCAN‐Clim: a tool to support pest climate suitability analysis based on climate classification
Bibliographic record
Abstract
EFSA pest categorisations and pest risk assessments include the assessment of the potential establishment of plant pests. Together with the presence of host plants, climate suitability analysis is an important element to analyse the likelihood of potential establishment of a pest in an area. One of the main approaches used in EFSA plant health risk assessment is the analysis based on climate classifications i.e. evidencing the occurrence of climates enhancing pest development and persistence in a specific area. SCAN-Clim is a tool designed to support climate suitability analysis based on climate classifications. The current version is the first prototype of the tool, developed in the R language, currently used to support EFSA climate suitability analysis for pest categorisation and for quantitative pest risk assessment. Tested on over 34 EFSA works, SCAN-Clim significantly improved the speed of climate suitability maps generation guaranteeing a standardised map format and providing documentation on input/outputs. Further improvements will include the development of an interactive web app accessible through the EFSA R4EU Portal (expected to be delivered in 2022).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.028 |
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".