The important characteristics of soil organic matter affecting 2,4-dichlorophenoxyacetic acid sorption along a catenary sequence
Bibliographic record
Abstract
Variations in the characteristics of soil organic matter (SOM) at the field-scale are largely unknown, particularly in relation to observed variations in herbicide sorption. For the herbicide 2,4-D [2,4-dichlorophenoxyacetic acid], we found that its organic carbon-normalized sorption coefficient, Koc, varied by four-fold, from 76 to 315 L kg(-1), in the Ap-horizon along a slope transect in an undulating agricultural field in Manitoba, Canada. In order to explain the relatively large in-field variation in 2,4-D Koc values, techniques ranging from conventional chemical fractionation methods to solid state Cross Polarization and Magic-Angle Spinning (13)C-Nuclear Magnetic Resonance applied on whole soils, were used to derive SOM chemical, physical and structural parameters for correlation analyses with the measured 2,4-D Koc values on whole soils. Out of the 15 parameters considered, the 2,4-D Koc was significantly positively correlated with 1) the carbon (C) content of sodium hydroxide-extracted humic acids (r = 0.83, P < 0.01), a chemical parameter indicative of free form C in soil; 2) the molar absorptivity of humic acids at wavelength 280 nm (r = 0.81, P < 0.01), a physical parameter indicative of greater SOM aromaticity; and 3) the relatively intensity of aryl C (r = 0.92, P < 0.01) and O-aryl C (r = 0.93, P < 0.01) in whole soil, both structural parameters indicative of aromatic C. Consequently, the results suggest that in-field variations in 2,4-D Koc values are induced by variations in SOM aromaticity. Koc values are among the most sensitive parameters in herbicide fate models used in regulatory and environmental assessments. Currently, these herbicide fate models do not consider associations between SOM characteristics and Koc and hence revising model equations to include these associations may improve estimates of herbicide persistence, bioavailability and transport at the field-scale.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".