Bibliographic record
Abstract
Hair fescue (Festuca filiformis Pourr.) is a tuft-forming perennial grass of concern in lowbush blueberry (Vaccinium angustifolium Ait.) as tufts form dense sods that reduce lowbush blueberry yield and inhibit harvest. Although generally tolerant to ACCase-inhibiting herbicides, injury to some Festuca spp. following clethodim applications has been reported. ACCase-inhibiting herbicides are important for non-bearing and bearing year perennial grass management in lowbush blueberry but have not been extensively evaluated for hair fescue management. The objectives of this research were to (1) determine the relative efficacy of foramsulfuron, fluazifop-P-butyl, sethoxydim, and clethodim on hair fescue, and (2) determine if foramsulfuron tank mixtures with fluazifop-P-butyl, sethoxydim, and clethodim improve hair fescue suppression. None of the herbicides evaluated caused unacceptable injury to lowbush blueberry. Foramsulfuron (35 g a.i. ha −1 ) reduced hair fescue total tuft density, flowering tuft density, and flowering tuft inflorescence number. Fluazifop-P-butyl (250 g a.i. ha −1 ) and sethoxydim (495 g a.i. ha −1 ) caused variable levels of visual injury to hair fescue and did not reduce total tuft density, flowering tuft density, or flowering tuft inflorescence number. Clethodim (91 g a.i. ha −1 ), however, caused >50% visual injury to hair fescue and reduced hair fescue total tuft density, flowering tuft density, and flowering tuft inflorescence number. Fluazifop-P-butyl and sethoxydim tank mixtures with foramsulfuron did not increase hair fescue suppression relative to foramsulfuron alone. A foramsulfuron + clethodim tank mixture provided equivalent hair fescue suppression as either herbicide applied alone. Foramsulfuron and clethodim should therefore be used in rotation rather than tank mixture to manage hair fescue in lowbush blueberry.
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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 | 0.001 | 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".