Tillage, Intercrop, and Controlled Drainage–Subirrigation Influence Atrazine, Metribuzin, and Metolachlor Loss
Bibliographic record
Abstract
Atrazine (6-chloro-N2-ethyl-N4-isopropyl-1,3,5-triazine-2,4-diamine) and metolachlor [2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl)acetamide] have been found with increasing occurrence in rivers and streams. Their continued use will require changes in agricultural practices. We compared water quality from four crop-tillage treatments: (i) conventional moldboard plow (MB), (ii) MB with ryegrass (Lolium multiflorum Lam.) intercrop (IC), (iii) soil saver (SS), and (iv) SS + IC; and two drainage control treatments, drained (D) and controlled drainage-subirrigation (CDS). Atrazine (1.1 kg a.i. ha-1), metribuzin [4-amino-6-(1,1-dimethylethyl)-3-(methylthio)-1,2,4-triazine-5(4H)-one] (0.5 kg a.i. ha-1), and metolachlor (1.68 kg a.i. ha-1) were applied preemergence in a band over seeded corn (Zea mays L.) rows. Herbicide concentration and losses were monitored from 1992 to spring 1995. Annual herbicide losses ranged from < 0.3 to 2.7% of application. Crop-tillage treatment influenced herbicide loss in 1992 but not in 1993 or 1994, whereas CDS affected partitioning of losses in most years. In 1992, SS + IC reduced herbicide loss in tile drains and surface runoff by 46 to 49% compared with MB. The intercrop reduced surface runoff, which reduced herbicide transport. Controlled drainage-subirrigation increased herbicide loss in surface runoff but decreased loss through tile drainage so that total herbicide loss did not differ between drainage treatments. Desethyl atrazine [6-chloro-N-(1-methylethyl)-1,3,5-triazine-2,4-diamine] comprised 7 to 39% of the total triazine loss.
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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 | 0.000 | 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) | 0.001 | 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".