Numerical Analysis of Transport of Trifluralin From a Subsurface Dripper
Bibliographic record
Abstract
The transport of a pulse of trifluralin (2,6‐dinitro‐N,N‐dipropyl‐4‐[trifluoromethyl] benzenamime) applied via a subsurfacedripper was analyzed numerically. Results of the analyses suggest that the movement and spread of trifluralin in the soil is considerably retarded by its strong adsorption to the solid phase of the soil. This is particularly so in soils which contain aconsiderable fraction of organic C and in fine‐textured (clayey) soils with low hydraulic conductivity and high water retentivity. Water uptake by plant roots and the resultant rapid decrease of water velocity with increasing distance from the dripper restricts further the downward movement of trifluralin and its potential to pollute the groundwater. The presence of dissolved organic matter (DOM) in the irrigation water may enhance both the movement and the spread of trifluralin in the soil, particularly in coarse‐textured soils with a relatively small fraction of organic C. Because of the strong adsorption of trifluralin to the soil, its concentration in the aqueous phase of the soil is very low and it decreases further with increasing time because of degradation and nonequilibrium sorption. This is particularly so in coarse‐textured soils with a relatively large fraction of organic C. Nevertheless, results of the analyses suggest that for soils of quite widely differing textures and organic C contents, a trifluralin concentration of may persist in the vicinity of the dripper for a relatively long period of time (90 d) even with relatively small applied mass (3 × 10−5 kg).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".