Use of SOFEA to Predict 1,3‐D Concentrations in Air in High‐Use Regions of California
Bibliographic record
Abstract
Methyl bromide, a commonly used soil fumigant, is being phased out per the Montreal Protocol and multiple fumigants are being positioned as replacements. Most effective soil fumigants, including methyl bromide, have the potential for inhalation exposure if the material volatilizes from soil. Chronic exposures for the fumigant 1,3-dichloropropene (1,3-D) are managed in part by the California Department of Pesticide Regulation by limiting the annual amount that can be used within a given township. A stochastic/deterministic numerical system (SOil Fumigant Exposure Assessment system [SOFEA]) was developed using the USEPA air dispersion model ISCST3, field study observations for flux loss, and links to Geographic Information Systems (GIS). SOFEA was used retrospectively to simulate concentrations of 1,3-D in air for direct comparison with monitoring program observations conducted by California Air Resources Board in Fresno County. These results indicated slight overprediction but correct magnitudes for regional air concentrations, especially at the higher percentiles, and provide a performance test. SOFEA was also used, prospectively, to predict air concentrations in potential future-use scenarios. These simulations of chronic air concentrations in two high-use 1,3-D counties of California (Ventura, Merced) consisted of 25 contiguous townships treated either at 1.5 times the current township allocation (40,937 kg) or at the maximum levels of 1,3-D used between 1999 and 2006. Exposure predictions for large regions are necessary to evaluate chronic population-based lifetime exposure and risk to 1,3-D should use patterns change. SOFEA provides a tool to estimate regional air concentrations within high-use areas required for such risk assessments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".