Profiling of Plant Clinic Users
Bibliographic record
Abstract
It is estimated that 26% to 40% of the world's potential crop production is lost each year because of weeds, pests and diseases (OECD-FAO, 2012).Unfortunately, the limited use of crop protection practices, coupled with the changing climate (risk of new pest introductions) and increasing trade in a globalized world (risk of pests moving across borders and regions) are likely to exacerbate this situation.The CABI-led Plantwise programme is contributing to global efforts to mitigate losses from crop health problems and improve rural livelihoods by helping farmers in over 30 countries to lose less of their crops.A key component of the Plantwise programme is the establishment of plant clinics, which are meeting places (mostly operating regularly near local markets) where farmers who are struggling with plant pests and diseases can send samples of their 'sick' crops for diagnosis and plant health advice.Based on the need to understand which types of farmers plant clinics are currently reaching, this study was conducted with the objective of profiling plant clinic users.Profiling the plant clinic users can be helpful in any attempt to prioritise and target farmers with certain characteristics that align with the objectives of Plantwise. Highlights• The purpose of this study is to understand the types of farmers Plantwise is currently reaching so as to inform decisions on whether to focus or change methods to reach a particular profile of farmers.• The study is based on available Plantwise-related socio-economic survey datasets.• Characteristics of a typical household that visit plant clinics include middle-aged male head of household with low education attainment, small land holdings with secure tenure, low asset accumulation, limited off-farm employment opportunities, and low participation in farmer group activities.• Compared with other farmers in similar environments (i.e., non-clinic users), plant clinic users are relatively "asset-rich" and are slightly better educated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".