Subjectivity of visual assessments in FOCUS kinetics and acceptability of first-order fits for regulatory modelling
-д хадгалсан:
| -д хэвлэсэн: | Environmental Sciences Europe vol. 36, no. 1 (Dec 2024), p. 187 |
|---|---|
| Үндсэн зохиолч: | |
| Бусад зохиолчид: | , , , |
| Хэвлэсэн: |
Springer Nature B.V.
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| Нөхцлүүд: | |
| Онлайн хандалт: | Citation/Abstract Full Text - PDF |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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MARC
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|---|---|---|---|
| 001 | 3118391251 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2190-4715 | ||
| 024 | 7 | |a 10.1186/s12302-024-01013-5 |2 doi | |
| 035 | |a 3118391251 | ||
| 045 | 2 | |b d20241201 |b d20241231 | |
| 084 | |a 165828 |2 nlm | ||
| 100 | 1 | |a Rödig, Edna |u Syngenta Agro GmbH, Frankfurt, Germany; Syngenta, Jealott’s Hill International Research Centre, Bracknell, UK (GRID:grid.426114.4) (ISNI:0000 0000 9974 7390) | |
| 245 | 1 | |a Subjectivity of visual assessments in FOCUS kinetics and acceptability of first-order fits for regulatory modelling | |
| 260 | |b Springer Nature B.V. |c Dec 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The degradation half-life (DegT50) of a substance in soil plays an important role in the approval process of a plant protection product and is a sensitive input parameter for regulatory models. It is usually derived through least squares optimizations of mathematical models to measured degradation data according to EU FOCUS degradation kinetics guidance. A strong consensus on degradation parameters provides a solid foundation for parts of the environmental risk assessment. The DegT50 of a substance for regulatory modeling is preferably derived from a single first-order (SFO) model as this is currently the only kinetic model implemented in EU regulatory models of the environmental fate of pesticides. However, kinetic optimisation tools do not always provide a regulatory acceptable SFO fit even though a visual inspection of the data suggests it may be possible. It was therefore hypothesized that more acceptable SFO fits might be achieved by adapting the objective function that is minimized during the optimization.Eight objective functions with varying weightings were tested on 29 laboratory soil degradation datasets. A web-based app was developed to allow experts in environmental safety of plant protection products to visually assess the goodness of fits resulting from different objective functions. The visual assessments and a quantitative metric, newly introduced in the proposed update of the FOCUS guidance, show that the acceptability of SFO fits can be increased, but no single objective function exclusively improves all fits. The assessment reveals that expert judgment is very subjective. Participants tended to change their mind when judging the acceptance of a fit, assumingly caused by a learning curve or a period of calibration.It is concluded that different objective functions could be considered in the kinetic assessment as it can improve the acceptability of SFO fits and hence endpoints for regulatory modeling. This study reveals that various qualitative factors influence the visual judgment of experts when performing a kinetic modeling assessment. The proposed quantitative metric seems to be in alignment with the visual assessment of fits to derive modeling endpoints and a promising step toward less subjective kinetic modeling assessments. | |
| 653 | |a Pesticides | ||
| 653 | |a Plant protection | ||
| 653 | |a Environmental assessment | ||
| 653 | |a Visual perception | ||
| 653 | |a Assessments | ||
| 653 | |a Visual discrimination learning | ||
| 653 | |a Parameter sensitivity | ||
| 653 | |a Mathematical models | ||
| 653 | |a Kinetics | ||
| 653 | |a Objective function | ||
| 653 | |a Soil degradation | ||
| 653 | |a Learning curves | ||
| 653 | |a Environmental risk | ||
| 653 | |a Visual aspects | ||
| 653 | |a Risk assessment | ||
| 653 | |a Acceptability | ||
| 653 | |a Surface water | ||
| 653 | |a Environmental science | ||
| 653 | |a Optimization | ||
| 653 | |a Subjectivity | ||
| 653 | |a Parameter estimation | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Ford, Simon |u Battelle UK Limited, Essex, UK (GRID:grid.426114.4) | |
| 700 | 1 | |a Bailey, Andrew D. |u Capgemini Engineering, Hybrid Intelligence, Abingdon, UK (GRID:grid.432202.5) (ISNI:0000 0004 0626 2888) | |
| 700 | 1 | |a Bird, Michael |u Syngenta, Jealott’s Hill International Research Centre, Bracknell, UK (GRID:grid.426114.4) (ISNI:0000 0000 9974 7390) | |
| 700 | 1 | |a Patel, Mitesh |u Syngenta, Jealott’s Hill International Research Centre, Bracknell, UK (GRID:grid.426114.4) (ISNI:0000 0000 9974 7390) | |
| 773 | 0 | |t Environmental Sciences Europe |g vol. 36, no. 1 (Dec 2024), p. 187 | |
| 786 | 0 | |d ProQuest |t Public Health Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3118391251/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3118391251/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |