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 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 | 0.001 | 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".