Evaluation of broadcast and spot herbicide applications for spreading dogbane (<i>Apocynum androsaemifolium</i> L.) management in lowbush blueberry fields
Bibliographic record
Abstract
Spreading dogbane is a creeping herbaceous perennial weed in lowbush blueberry. Management is limited primarily to spot applications of dicamba, though recent herbicide registrations facilitate the evaluation of new broadcast and spot herbicide applications. The objectives of this research were to determine (1) the effect of sequential postemergence (POST) mesotrione application interval on spreading dogbane, (2) the effect of sequential POST mesotrione and foramsulfuron applications on spreading dogbane, (3) the effect of POST herbicide tank mixtures on spreading dogbane, (4) the effect of summer and fall spot herbicide applications on spreading dogbane, and (5) the effect of spot applications of dicamba tank mixtures with sulfonylurea herbicides on spreading dogbane. Broadcast mesotrione (144 g a.i. ha−1) and foramsulfuron (35 g a.i. ha−1) applications did not control spreading dogbane. Control was not improved by sequential applications of either herbicide. Broadcast mesotrione + foramsulfuron applications reduced non-bearing-year density and may be more effective than either herbicide applied alone. Broadcast flazasulfuron applications reduced non-bearing-year shoot density and flazasulfuron + foramsulfuron applications reduced non-bearing-year and bearing-year shoot densities. Summer spot applications of foramsulfuron and flazasulfuron caused 70% injury to spreading dogbane but did not reduce shoot density, and dicamba continues to be the most effective spot herbicide treatment. Fall spot applications did not control spreading dogbane due to early senescence of spreading dogbane shoots. Spot applications of dicamba at 0.96 or 1.92 g a.e. L water−1 provided equivalent spreading dogbane control and efficacy was not improved by tank mixture with foramsulfuron, flazasulfuron, or nicosulfuron + rimsulfuron.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".