Soil and weather conditions associated with plant damage from post-emergent metribuzin in lentil (Lens culinaris) in southern Australia
Bibliographic record
Abstract
Multiple field experiments and a controlled-environment temperature study were conducted to investigate soil and weather conditions responsible for herbicide phytotoxicity in lentil (Lens culinaris Medik.) from post-emergent application of metribuzin. A linear relationship was observed between plant injury (% necrosis) and metribuzin rate in all 12 environments, but in only 11 environments for anthesis dry weight and nine environments for both plant density and grain yield. Grain-yield reduction from label metribuzin rates of 135 g a.i. ha–1 for sand and 285 g a.i. ha–1 for clay ranged from 0% to 32% and 0% to 67%, respectively, across all environments. Principal component analysis of soil and weather factors around the time of herbicide application suggested that metribuzin-induced plant damage in lentil was due to a combination of multiple soil and weather factors. However, heavy rainfall within 10 days of herbicide application, particularly on light-textured soils or where soil moisture was low, was most strongly linked to plant damage. Experiments targeting the impact of reductions in temperature post-metribuzin application showed no effect, and of light intensities pre- and post-metribuzin application showed low effects on plant-damage measures. Because rainfall in the 10 days after application is a major determinant of metribuzin damage in winter-grown lentil in southern Australia, a higher level of selective tolerance to metribuzin than that present in commercial cultivars is needed for its safe post-emergent use. Early and late measures of plant damage will be required to assess accurately plant tolerance to post-emergent metribuzin application in lentil.
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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 | 0.001 | 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".