Assessment And Prioritisation Of Pathways
Bibliographic record
Abstract
There is an increasing interest, both regulatory and scientific, in the pathways used by plant pests and diseases to spread to, and establish in, new locations across the globe. A pathway can be defined as ‘any means that allows entry and spread of a pest’ (IPPC 2018), covering both natural and human driven processes. With world trade continually evolving and new trade links between countries being formed on a regular basis, new pathways are being created and emerging pathways increase in importance. Biosecurity measures such as the application of quarantine and implementing trade restrictions are usually based on species specific risk assessments of known pests and diseases. However, the larger challenge comes from emerging pests and diseases, which often are not considered a problem in their native ranges due to the coevolution of plant defences and natural control by predatory and parasitic species. In a more connected world, pathways assessments can help protect against new and emerging pest species by identifying the generic risks associated with a pathway. The project is focussed on initiating a network of practitioners of pathway analyses for plant health. As a starting point for the network, the project partners considered addressing 4 objectives: Identify current systems and methodologies used to assess new and emerging horticultural trade pathways Identify knowledge gaps regarding current industry practices in exporting countries Develop proposals to overcome existing difficulties in assessing pathways Provide a report on options for the systematic evaluation and prioritisation of pathways The project held a workshop in Angers, France, made up of representatives of the five partner organisations from Europe and North America and invited contributors from the French agricultural research and international cooperation organization (CIRAD) and the European and Mediterranean Plant Protection Organization (EPPO). The European partners attended in person while the North American partners attended by video conference. Each partner organisation presented their work on pathways assessment, and discussions were held on the similarities and differences between the approaches of each of the partners. This was followed up with a videoconference to further develop and report on the ideas raised at the workshop. The project partners identified a series of knowledge gaps which would need addressing to allow pathways assessments to be more widely performed, along with suggestions for approaches to filling these knowledge gaps.
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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 | 0.039 | 0.075 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".