Near‐Infrared Spectroscopy as a Tool for Generating Sorption Input Parameters for Pesticide Fate Modeling
Bibliographic record
Abstract
Core Ideas NIRS can be integrated with pesticide fate models using a test set method. NIRS calibrations are most successful for pesticides with smaller K d values. Regional scale NIRS calibrations are most successful if developed on data sets of similar K d value range. Sorption parameters (such as K d values) are among the most sensitive input parameters in pesticide fate models. This study demonstrates that near‐infrared spectroscopy (NIRS), in combination with batch equilibrium techniques, can be used to estimate K d values, thereby increasing throughput of the many samples required to characterize spatial variability of pesticide sorption within fields. The Pesticide Root Zone Model version 3.12.2 (PRZM‐3) was used to compare scenarios that used NIRS spectral data, pedotransfer functions, and batch equilibrium methods as inputs for the calculation of 2,4‐dichlorophenoxyacetic acid (2,4‐D) and atrazine leaching in 591 soil horizons. Based on the 3564 simulation runs conducted, we concluded that the added benefit of NIRS is most useful when the pesticides under study have small sorption potentials and short half‐lives in soil. The 2,4‐D and atrazine sorption by soil was highly correlated to soil organic C (SOC) content in the fields under study. The feasibility of using NIRS to predict pesticide K d values largely relies on the sorption of the pesticide being significantly correlated to SOC. In addition, successful regional approaches to predicting K d values from NIRS spectral data can also be developed when the calibration model is derived by combining a set of fields where each has a similar statistical population characteristic in K d values.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".