Evaluation of acetolactate synthase (ALS)-inhibiting herbicides for red sorrel (<i>Rumex acetosella</i> L.) management in lowbush blueberry (<i>Vaccinium angustifolium</i> Aiton)
Bibliographic record
Abstract
Red sorrel is a common herbaceous perennial weed species in lowbush blueberry fields that may be managed with acetolactate synthase (ALS)-inhibiting herbicides. Greenhouse and field experiments were established to determine the crop tolerance and potential efficacy on red sorrel of tribenuron-methyl, nicosulfuron + rimsulfuron, foramsulfuron, flazasulfuron, pyroxsulam, and halosulfuron-methyl. Ramet density of greenhouse-grown red sorrel plants established from root fragments was reduced by tribenuron-methyl, flazasulfuron, and pyroxsulam, though tribenuron-methyl and flazasulfuron were the most consistently effective herbicides under field conditions. Spring non-bearing year tribenuron-methyl and flazasulfuron applications reduced both non-bearing and bearing year total red sorrel ramet density and reduced non-bearing year red sorrel flowering ramet and seedling density without injuring lowbush blueberry. Fall non-bearing year tribenuron-methyl and flazasulfuron applications reduced bearing year red sorrel total and flowering ramet density, and this application timing should be evaluated further to improve understanding of crop injury risks. Fall bearing year tribenuron-methyl and flazasulfuron applications reduced non-bearing year red sorrel total and flowering ramet density but did not reduce seedling density. Nicosulfuron + rimsulfuron, foramsulfuron, pyroxsulam, and halosulfuron-methyl efficacy on red sorrel were inconsistent or limited, and these herbicides are not recommended for red sorrel management in lowbush blueberry.
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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".