Evaluation of currently registered herbicides for fall bearing year red sorrel (<i>Rumex acetosella</i> L.) management in lowbush blueberry (<i>Vaccinium angustifolium</i> Aiton)
Bibliographic record
Abstract
Red sorrel is a common creeping herbaceous, perennial weed species in lowbush blueberry fields and reproduces asexually via ramets from creeping roots. Ramets emerge throughout the season but remain vegetative due to a vernalisation requirement for flowering. This weed may therefore be managed with fall herbicide applications, but few currently registered herbicides have been evaluated for fall red sorrel management in lowbush blueberry. The objectives of this research were to (i) determine the effect of various herbicide treatments on red sorrel shoot and root biomass in the greenhouse, (ii) determine the effect of fall bearing year herbicide applications on overwintered red sorrel ramet density in the field, (iii) determine if reduced overwintered ramet density reduces flowering ramet density in the field, and (iv) estimate the potential for red sorrel to recover from fall bearing year herbicide applications. Dicamba, tribenuron-methyl, and glufosinate reduced red sorrel shoot and root biomass in the greenhouse and reduced overwintered ramet density in the field. Clopyralid, sulfentrazone, and flumioxazin reduced shoot and root biomass in the greenhouse but exhibited limited efficacy in the field. Nicosulfuron + rimsulfuron and glyphosate were ineffective in both the greenhouse and field. Dichlobenil and pronamide reduced overwintered ramet density in the field. Reduction of overwintered ramet density did not consistently reduce flowering ramet density. Dichlobenil reduced seedling density at two sites, but no herbicide consistently reduced summer non-bearing year ramet density. Fall herbicide applications alone, therefore, do not appear to maintain red sorrel suppression in lowbush blueberry fields.
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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".