Evaluation of dichlobenil for hair fescue (<i>Festuca filiformis</i> Pourr.) management in wild blueberry (<i>Vaccinium angustifolium</i> Ait.)
Bibliographic record
Abstract
Abstract Hair fescue is an economically destructive, tuft‐forming weed common in wild blueberry fields. The current management of hair fescue is almost completely reliant on pronamide (3,5‐dichloro‐ N ‐[2‐methylbut‐3‐yn‐2‐yl]benzamide) herbicide with few other products demonstrating effectiveness. Dichlobenil (2,6‐dichlorobenzonitrile) is a group 29 herbicide registered for use in wild blueberry; however, it has seen limited use because of its high product cost of over CAD 1800 ha −1 and limited research on the effect of varying application rates. Treatments (dichlobenil at 4400, 5700, and 7000 g a.i. ha −1 , pronamide at 2240 g a.i. ha −1 , and a nontreated control) were applied during the 2020 autumn prior to wild blueberry stem emergence in the nonbearing year (2021). Each treatment was replicated five times per field across three fields in central Nova Scotia. Dichlobenil at 4400, 5700, and 7000 g a.i. ha −1 reduced 2021 (nonbearing year) living tuft density by 59%, 71%, and 83%, respectively, and provided residual control in 2022 (bearing year) with densities reduced by 59%, 69%, and 78%, respectively. Comparatively, pronamide reduced 2021 and 2022 living tuft density by 95% and 91%. Reductions in living tuft density resulted in yield increases of 62%, 57%, and 94% in plots treated with dichlobenil at 4400, 5700, and 7000 g a.i. ha −1 , respectively, and 73% for plots treated with pronamide. Results demonstrate that dichlobenil is an effective herbicide for managing hair fescue in wild blueberry. The potential for the selection of herbicide‐resistant biotypes to industry standard pronamide could be prolonged with integrated dichlobenil applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| 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 teacher head, 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".