Bibliographic record
Abstract
Abstract The environmental fate of applied agrochemicals can be estimated with either conventional concentration‐based or fugacity models. Although both types of models can predict the fate of chemicals, the fugacity models have several inherent advantages. Fugacity models express the chemical potential in diverse media in common units of pressure, and so the chemical equilibrium among media is expressed directly. This is particularly valuable when assessing the migration of the chemical among diverse media such as air, water, soil solids, plants, and animals. Chemical transport and transformation processes, such as advection, diffusion, and reaction, are quantified using D ‐values, which share identical units despite describing very different processes. This uniformity aids in identifying the important processes governing chemical fate in the environment, and it simplifies the formulation and interpretation of the modeling equations. Modeling and assessing the fate of agrochemicals in terms of fugacity as well as concentrations can contribute to a fuller understanding of agrochemical behavior. This article presents a brief conceptual overview of the fugacity framework, describes situations using malathion as an example, and provides references for further consultations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".