Dissipation of Bromide and Metribuzin Affected by Tillage and Crop Residue Management in Subarctic Alaska
Bibliographic record
Abstract
Prudent use of agricultural fertilizers and herbicides is paramount for sustaining or improving surface and ground water quality in Subarctic regions, but little information is available that documents the loss of chemicals from agricultural lands in the Subarctic. This study aimed to ascertain more clearly how time of application and land management practices affect the loss of bromide and metribuzin in a Subarctic soil. Potassium bromide (KBr), a surrogate for nitrate, and metribuzin, commonly used to control broadleaf weeds, were applied in the autumn of 1996 and the spring of 1997 to a silt loam that had been subjected to conventional tillage (CT), minimum tillage (disk once [DO]), and no tillage (NT) since 1983. Superimposed on the tillage treatments were the removal or retention of barley (Hordeum vulgare L.) stubble and loose straw. Loss of these chemicals was ascertained by sampling the soil profile at the time of heading of barley, before freeze-up of the soil in autumn, and after spring thaw until September 1998. Tillage and residue treatments did not influence the recovery of autumn-applied or spring-applied Br. However, recovery of Br diminished with time: about 30% of the Br applied in autumn and 45% of that applied in spring remained in the soil profile by September 1998. Tillage, but not residue, treatments influenced the recovery of metribuzin. Recovery of metribuzin at the termination of this study was 6% or more in NT soil and 2% or less in CT and DO soil; greater recovery in NT soil was presumably a result of slower degradation in NT than in CT and DO. This study suggests that bromide (and thus nitrate) and metribuzin are more prone to leaching when applied in autumn and that tillage practices affect retention of metribuzin, but not nitrate, in the soil of Subarctic Alaska.
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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 | 0.000 | 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 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".