Evaluation of broadcast and spot herbicide applications for narrowleaf goldenrod [<i>Euthamia graminifolia</i> (L.) Nutt.] management in lowbush blueberry
Bibliographic record
Abstract
Abstract Goldenrods are common perennial weeds in lowbush blueberry fields in Nova Scotia. Management options are limited to mowing and suppression with POST mesotrione applications. The objectives of this research were to (1) compare efficacy of single versus sequential nonbearing-year POST mesotrione applications on goldenrod (2) identify the optimal interval between sequential POST mesotrione applications (3) evaluate nonbearing-year POST bicyclopyrone applications on goldenrod, and (4) evaluate nonbearing-year summer and fall herbicide spot treatments on goldenrod. POST mesotrione applications at 144 g ai ha−1 caused 39% to 77% injury but did not reduce goldenrod shoot density. In contrast, mesotrione applications at 144 g ai ha−1 followed by sequential mesotrione application at 14, 21, or 28 days after initial treatment caused greater than 90% injury to goldenrod and reduced both nonbearing- and bearing-year shoot density. POST bicyclopyrone applications at 50 g ai ha−1 caused 69% to 80% injury to goldenrod but did not reduce shoot density. A bicyclopyrone plus mesotrione tank mixture did not improve goldenrod control relative to mesotrione or bicyclopyrone alone. Summer spot applications of glyphosate (7.24 g ae L water−1), glufosinate (0.75 g ai L water−1), and mesotrione (0.72 g ai L water−1) consistently injured goldenrod and reduced both nonbearing- and bearing-year shoot density. Summer spot applications of bicyclopyrone (0.25 g ai L water−1), flazasulfuron (0.31 g ai L water−1), dicamba (1 g ae L water−1), dicamba plus diflufenzopyr (0.7 g ae L water−1 plus 0.3 g ai L water−1), triclopyr (1.68 g ai L water−1), clopyralid (0.08 g ai L water−1), tribenuron methyl (0.2 g ai L water−1), and foramsulfuron (0.2 g ai L water−1) injured goldenrod but did not consistently reduce shoot density. When these herbicides were evaluated as fall spot applications, only glyphosate reduced goldenrod shoot density in the year after application.
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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".