Deposition of Pesticide to the Soil and Plant Retention During Crop Spraying: The Art State
Bibliographic record
Abstract
Environment pollution related to pesticides has been confirmed by the scientific community for a long time, but the available information on the impact of this phenomenon on human health and the ecosystem are still insufficient. Contamination of the environment can occur through various pathways, ground deposits during the application of pesticides to crops are one of those pathways. The retention rate of sprayed droplets is an important factor both for the efficacy of the phytosanitary treatment and the quantities of lost pesticides on the ground. This paper presents an overview of factors that affect spray droplet behavior, involved process in sprayed drop fate and the mainly techniques for measuring pesticide deposits to the ground and plant retention. We present studies that have focused on pesticide retention and soil deposition during crop spraying in relation to sprayer equipment, used formulation and climatic factors. Plant retention and pesticide deposits during spray is a complex problem depending mainly on physical sprayer parameters, climatic conditions, spray properties and chemical formulation. By illustrating the large variety of analytical methods during the recently years, it aims to explain that it is always possible to assess the impact of pesticides in all compartments of the environment. The use of concerned molecules and natural collectors is the safest to evaluate the real situation, but the use of tracers and artificial targets is both very reported and safety for the environment.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".