Assessing the Fate of an Aromatic Hydrocarbon Fluid in Agricultural Spray Applications Using the Three-Stage ADVOCATE Model Framework
Bibliographic record
Abstract
Components of emulsifiable concentrates (ECs) used in pesticide formulations may be emitted to air following application in agricultural use and contribute to ozone formation. A key consideration is the fraction of the ECs that is volatilized. This study is designed to provide a mechanistic model framework for estimating emissions of an aromatic hydrocarbon fluid used in ECs based on the results of spray chamber experiments that simulate fate as the fluids become subject to volatilization, sorption to soil, and biodegradation. The results indicate the need to treat the volatilization losses in three stages: (i) losses during spraying, (ii) losses up to 12 h after spraying in which the soil is coated with the ECs, and (iii) subsequent longer term losses in which the ECs become increasingly sorbed and subject to biodegradation. A mass balance model, the agrochemical derived volatile organic compound air transfer evaluation (ADVOCATE) tool, is developed, treating the ECs as seven hydrocarbon component groups, to estimate the volatilization and biodegradation losses using parameters fitted to empirical data. This enables losses to be estimated for each hydrocarbon component under field conditions, thereby providing a basis for improved estimation of ozone formation potential and for designing ECs that have lower emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".