Use of Two Sulfonyl Urea Herbicides in Lowbush Blueberry
Bibliographic record
Abstract
SUMMARY The continuous use of the broad spectrum herbicide hexazinone since 1981 has resulted in many changes to the weedy flora of lowbush blueberry fields, including shifts to hexazinone-tolerant species. Many of these occur in patches and could best be controlled by selective, foliar herbicide treatments. Preliminary assessments of several sufonyl urea (SU) herbicides indicated that tribenuron (Spartan or Express 75% DF) was effective against some important weed species with little risk to the crop. Long-term control (> 95%) of eastern bracken [Pteridium aquilinum (L.) Kuhn], yellow loosestrife [Lysimachia terrestris (L.) BSP], speckled alder [Alnus incana (L.) Moench], common wild rose (Rosa virginiana Mill.) and several others was obtained with mid summer, prune-year foliar applications of tribenuron at 0.2 g L_1 water with 0.2% Agral 90. Screening trials targeting black bulrush (Scirpus atrovirens Willd.), currently the most serious weed problem in Nova Scotia blueberry fields, indicated that SU's with activity against grasses were also effective against this weed, and a 1:1 commercial mixture of nicosulfuron/rimsulfuron (Ultim DF) was selected for further work. Summer and fall prune-year applications of nicosulfuron/rimsulfuron at 0.031 g L_1 water with 0.2% Agral 90 generally provided > 90% control, except when plants were stressed by drought. Although blueberry plants were injured (< 20% injury) when sprayed directly with these SU herbicides, there was little effect on plants in the understory following applications to weeds. Both herbicides have become registered for use in lowbush blueberry in Canada for control of the above species.
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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) | 0.001 | 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".