Methods to determine spatial variations of herbicide and estrogen sorption coefficients in undulating to hummocky terrains for pesticide fate modeling at the large scale
Bibliographic record
Abstract
To reduce the uncertainty associated with pesticide fate model predictions on the large scale, a rapid method is needed that can generate sorption coefficients (Kd values) with sufficient spatial detail. The feasibility of near-infrared spectroscopy (NIRS) to act as such a method was examined, using weak-acidic (2,4-D), weak-basic (atrazine) and zwitterion (glyphosate) herbicides and the natural steroid estrogen (17β-estradiol). A total of 609 horizons in 140 soil profiles were collected in agricultural fields near Brandon, Manitoba and near Saskatoon, Saskatchewan. In both agricultural fields, Kd values in horizons generally increased in the order of 2,4-D < atrazine < 17β-estradiol < glyphosate. Soil organic carbon content (SOC) followed by the soil pH were the major factors controlling the sorption of 2,4-D, atrazine and 17β-estradiol but glyphosate showed very strong sorption to soil particles regardless of measured SOC and soil pH values. For the chemicals studied, Kd values decreased from A to C horizons regardless of the segment of the slope from which the soil samples were collected, with the exception of glyphosate that showed relatively large Kd values in B-horizons illuviated with clay. Both the Zeiss Corona and the Foss 6500 spectrophotometers produced significantly strong predictive models for soil properties and Kd values of 2,4-D, atrazine and 17β-estradiol. However, models for glyphosate Kd values were weak or not significant. Using a test set approach and either soil spectral or soil properties data as independent variables, partial least squares regressions were successfully developed to estimate Kd values for use in the Pesticide Root Zone Model (PRZM) to calculate the herbicide mass leached. The study concluded that the added benefit of NIRS will be most useful if the pesticides under study have small sorption potentials and short half-lives in soil. Regional approaches to predicting Kd values from NIRS spectral data can also be developed if the calibration model is derived by combining a set of fields where each has a similar statistical population characteristic in Kd values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".