Preferential Bromide and Pesticide Movement to Tile Drains under Different Cropping Practices
Bibliographic record
Abstract
Subsurface drainage systems are useful tools to study chemical leaching in soils. Our objective was to compare the breakthrough behavior of bromide, atrazine (2-chloro-4-ethylamino-6-isopropylamino-s-triazine) and metolachlor [2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl) acetamid] to tile drains under two fall tillage practices (conventional tillage [CT] with a moldboard plow, and reduced tillage [RT] with a chisel plow) in field plots cultivated with corn (Zea mays L.). Leachate volume were greater in RT than in CT, with no statistical differences. Soil analysis showed that bromide migrated deeper in the soil profile than both herbicides, with little tillage effect. All chemicals were detected in drainage water at the same time and followed an event-driven behavior. Tillage had no effect on atrazine and metolachlor found in drainage water, while bromide concentration peaks were higher in RT than in CT in 1999. Concentration peaks were recorded earlier for atrazine and metolachlor than for bromide. Plots of cumulative relative chemical mass (cumulative mass divided by total mass measured in drainage) as a function of cumulative drainage were mostly linear for bromide, while they were S-shaped for both herbicides. Drainage that corresponded to 50% of relative cumulative mass ranged from 40 to 55% for bromide and from 5 to 28% for both herbicides. Rapid chemical movement to tile drains suggested that preferential flow was important in both CT and RT, and that these tillage practices had little influence on this phenomena.
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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) | 0.000 | 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".