Evaluation of amino acid–inhibiting herbicide mixtures for hair fescue (<i>Festuca filiformis</i>) management in lowbush blueberry
Bibliographic record
Abstract
Abstract Hair fescue is a perennial grass weed in lowbush blueberry fields that forms dense sods and reduces yield. As a result of natural tolerance or resistance of this grass to other currently registered herbicides growers rely on preemergence (PRE) applications of pronamide and postemergence (POST) applications of the Group 2 herbicides foramsulfuron and nicosulfuron + rimsulfuron for hair fescue management. This causes repeated application of Group 2 herbicides, which is compounded by the recent registration of flazasulfuron for POST suppression of hair fescue in lowbush blueberry. Mixtures of Group 2 herbicides with the amino acid–inhibiting herbicides glyphosate (Group 9) and glufosinate (Group 10), however, can improve weed control and may delay herbicide resistance development. This research used a factorial arrangement of Group 2 herbicides (none, foramsulfuron [35 g ai ha−1], nicosulfuron + rimsulfuron [13 + 13 g ai ha−1], flazasulfuron [50 g ai ha−1]) and mixtures (none, with glyphosate [902 g ae ha−1], and with glufosinate [750 g ai ha−1]) to identify possible mixtures that improve weed control and delay resistance development. Herbicides were applied in spring nonbearing year, fall bearing year, and fall nonbearing year, with each application timing conducted as a separate experiment. Foramsulfuron and nicosulfuron + rimsulfuron were not effective as fall applications, and spring applications of these herbicides with glyphosate or glufosinate improved hair fescue suppression. Glyphosate and glufosinate were more effective as fall rather than spring applications. Flazasulfuron was effective across all application timings, although its mixture with glufosinate generally improved hair fescue suppression. Flazasulfuron + glufosinate is tentatively recommended as an effective mixture for management of spring nonbearing-year and fall bearing-year hair fescue in lowbush blueberry.
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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 | 0.001 | 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".