Effect of application method on dichlobenil efficacy of hair fescue (<i>Festuca filiformis</i>) in lowbush blueberry
Bibliographic record
Abstract
Abstract This study assessed the potential of using dichlobenil to manage hair fescue in lowbush blueberry crops when targeted or broadcast-applied (7,000 g ai ha −1 ) as justification for developing a precision-targeted applicator. A randomized complete block design was used to assess both application methods, and results were compared with industry-standard propanamide (2,240 g ai ha −1 ). Targeted and broadcast-applied dichlobenil in fall 2020 significantly reduced average total tuft density in the nonbearing year (2021) by 75% and 67%, respectively, and in the bearing year (2022) by 61% and 59%, respectively. Broadcast pronamide applications in fall 2020 significantly reduced total tuft density by 84% in the nonbearing year (2021) and 81% in the bearing year (2022). These reductions in total tuft density resulted in average lowbush blueberry yields of 416, 557, 573, and 617 g m −2 for the control, pronamide applications, and targeted and broadcast-applied dichlobenil, respectively. Increases in yield were not significant, though the large variation within the sample is the probable cause. The similarities between targeted and broadcast-applied treatments demonstrate the potential of using targeted dichlobenil. Given the high product cost of dichlobenil at Can$1,873 ha −1 , hair fescue’s non-uniform distribution in lowbush blueberry fields and the lowbush blueberry industry’s overreliance on pronamide, targeted application of dichlobenil has significant potential. This work justifies the development of a mechanized precision-targeted applicator for use in lowbush blueberry cropping systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".