Study of Rainfastness of Synthetic Pesticides Using Newly Developed Rainfastness Adjuvant
Bibliographic record
Abstract
The development of rainfastness adjuvants is of great significance in reducing the loss of applied pesticides via rainfall and enhancing their efficacy. Most pesticides lose their efficiency after been exposed to 5 cm of rain. The use of rainfastness adjuvants reduces the number of times a pesticide must be applied to the field and, as a result, the overall quantity of pesticides used by a farmer. This not only reduces the costs incurred by farmers but also provides them with tools to enhance the sustainability of their operations. Also, enhancing the rainfastness of pesticides reduces the amount of pesticide runoff a farm operation generates. Two different mechanisms can provide rainfastness: fast uptake by the plant and strong adhesion to the plant surface. We studied a newly developed rainfastness adjuvant (Dow) that provides a significant improvement in rainfastness for multiple synthetic pesticides formulations. The newly developed adjuvant was added to the synthetic pesticides at a 7% concentration, and then the formulations were exposed to simulated rain at a flow rate of 6 L/h for different exposure times. An increase in active retention of up to 82% could be obtained with the newly developed adjuvant when compared to a control formulation. In addition, an increase in performance of up to 64% could be obtained with the adjuvant when compared to a commercial benchmark rainfastness adjuvant.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 0.000 |
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".