Potential Role of Sequential Glufosinate and Foramsulfuron Applications for Management of Fescues (<i>Festuca</i>spp.) in Wild Blueberry
Bibliographic record
Abstract
Sheep and hair fescue are perennial, tuft forming grasses that spread by seed and form dense sods in wild blueberry fields. These sods compete with the crop for resources and hinder harvest. Field and greenhouse studies were conducted in 2015 to evaluate 1) the effect of sequential glufosinate and foramsulfuron applications on suppression of fescues in the greenhouse and field, and 2) efficacy of glufosinate and foramsulfuron on fescue seedlings when applied at 2, 4, 6, and 8 wk after seedling emergence in the greenhouse. Glufosinate applications at 750 and 1,005 g ai ha−1followed by foramsulfuron application at 35 g ai ha−1reduced fescue leaf number and biomass relative to foramsulfuron application alone in the greenhouse. In the field study, fescue flowering tuft density, tuft inflorescence height, seed production, and seed viability were reduced by foramsulfuron alone, but there was a trend towards lower seed production and tuft height when fescues were treated with glufosinate at 1,005 g ha−1followed by foramsulfuron. Foramsulfuron caused low seedling mortality at all application timings evaluated, but glufosinate caused >90% mortality in seedlings when applied at 2, 4, 6, or 8 wk after seedling emergence. Our results suggest that sequential applications of these herbicides are less effective under field conditions relative to results obtained in the greenhouse, though burndown glufosinate applications may have a role in reducing fescue seedling recruitment. Additional research should be conducted to determine the effect of early spring and autumn glufosinate applications on fescue seedling recruitment and suppression of established fescue tufts with subsequent foramsulfuron applications.
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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".