Impact of metribuzin dose and water stress on chickpea plant health
Bibliographic record
Abstract
Chickpea is an important rotational pulse crop for Saskatchewan field crops. Since 2019, Saskatchewan chickpea production has suffered from a plant health issue following rainfall events around flowering to early podding resulting in apical wilting and eventual branch death. Post-emergence broadleaf weed management in chickpea relies on metribuzin, which can result in variable injury in chickpea. The study objective was to evaluate the effect of metribuzin dose and water stress on chickpea health in the greenhouse. At two weeks after treatment (WAT), chickpea mortality reached 25% for 206 g ai ha−1 (label rate) metribuzin dose and 56% for 413 g ha−1 dose, respectively. Maximum chickpea vegetative chlorosis and necrosis was 81% for the 413 g ha−1 metribuzin treatment and 65% for the 206 g ha−1 treatment. Chickpea longest branch length was reduced from 34 to 26 cm at 8 WAT with the 206 g ha−1 metribuzin treatment. Three weeks of permitting the soil to reach the wilting point and induce symptoms reduced chickpea longest branch length from 38 to 24 cm by 8 WAT. Chickpea pod production was reduced by 75% with the 206 g ha−1 metribuzin treatment and 90% with the water stress regime. Metribuzin at labeled dosing in Saskatchewan induced plant death and considerable vegetative necrosis when evaluated in the greenhouse.
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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 | 0.001 |
| 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".