Effects of Landscape Position, Rainfall, and Tillage on Residual Herbicides
Bibliographic record
Abstract
The effect of soil properties and weather on herbicide persistence and injury to following crops were studied at a site near Lethbridge, Alberta, Canada, with undulating topography that included no-tillage and conventional tillage systems on adjacent fields. Soil pH ranged from 5.2 (lower slope no-tillage) to 7.8 (upper slope conventional tillage) and soil organic matter content ranged from 2.3% (upper slope conventional tillage) to 4.4% (lower slope no-tillage). During the years when the experiments were conducted rainfall ranged from < 50% of normal to > 150% of normal. During dry years atrazine and metsulfuron severely injured wheat and lentil crops, seeded 1 yr after herbicide application, on upper slope locations. The most severe injury occurred on the upper slope conventional tillage location. In years with high rainfall, no crop injury occurred 1 yr after atrazine and metsulfuron application on either upper or lower slope locations in both tillage systems. Imazamox plus imazethapyr caused almost 100% injury in the lower slope position in the no-tillage system (pH 5.2) in the driest year. Following-crop injury due to the imidazolinone herbicides decreased with increasing rainfall and increasing soil pH. The most severe injury to following crops seemed to occur when herbicide dissipation was dependent on microbial activity and rainfall was below normal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".