Industry (UAPASTF) Response to Pesticide Regulators’ “State of the Knowledge” Review of Drone Use for Pesticide Application: Best Practices for Safe and Effective Application of Pesticides
Bibliographic record
Abstract
The Organization of Economic Cooperation and Development Working Party on Pesticides (OECD WPP) Drone/UAV Subgroup published a “state of the knowledge” report on pesticide application using unmanned aerial vehicles (UAV) in 2021. One of the recommendations made in this report was to “develop and publish a user-friendly summary of best practices (including the essential nature of calibration), pitfalls and a trouble shooting guide (both for generating trials data and applying pesticides in practice)”. In response to recommendations in that report, the pesticide registrant industry in the United States formed the global Unmanned Aerial Pesticide Application System Task Force (UAPASTF). This report outlines the overview of the “Best Management Practices” (BMP) guidance that was developed by the UAPASTF. UAV-based spraying of crop protection products is relatively new for most of the regions globally. Therefore, this guidance document is intended to serve as an excellent resource for growers, researchers (both academics and industry) and other relevant stakeholders to carry out UAV-based spray application in an efficient and safe manner.
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.
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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".