Sulfuryl Fluoride and Propylene Oxide as Fumigants for Stored-Product Pest Control
Bibliographic record
Abstract
Globally, fumigation stands out as a highly effective method for the control of pests in stored products. The phase-out of methyl bromide (MB) and phosphine, as mandated by the Montreal Protocol and growing resistance development of insects to phosphine, respectively, has prompted exploration of alternative fumigants. Sulfuryl fluoride and propylene oxide have emerged as promising alternatives due to their elevated toxicity levels at short exposure time and reduced risk to the environment. This chapter synthesizes research on the use of sulfuryl fluoride and propylene oxide for fumigation treatments of stored products, discussing their mode of action, their effectiveness against stored-product pests, and the dynamics of sorption, desorption, and residues in treated food products. The chapter also explores a way to overcome the drawbacks of the limited ovicidal effect of sulfuryl fluoride and the flammability of propylene oxide through co-fumigation and sequential fumigation. Sulfuryl fluoride and propylene oxide are often used in combination with carbon dioxide, phosphine, and vacuum. These strategies are designed to enhance the effectiveness of fumigation treatments while concurrently reducing the fumigant concentrations. Moreover, the adoption of integrated pest management approaches contributes to diminishing the dependence on a single chemical as a fumigant, thereby strengthening overall pest control measures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".