Evaluation of terbacil-based herbicide treatments for hair fescue (<i>Festuca filiformis</i>) management in lowbush blueberry
Bibliographic record
Abstract
Abstract Hair fescue is a common tuft-forming perennial grass weed that reduces yields and hinders mechanical harvest in lowbush blueberry fields. PRE terbacil applications traditionally controlled hair fescue but currently only provide suppression in most fields. Terbacil use has not, however, been evaluated in conjunction with other currently registered herbicides in lowbush blueberry. The objective of this research was to evaluate a range of terbacil-based herbicide treatments for hair fescue management in lowbush blueberry. The experiment was conducted at three lowbush blueberry fields in Nova Scotia, Canada. Spring nonbearing-year terbacil applications (2,000 g ai ha –1 ) exhibited variable efficacy on hair fescue with reduced total tuft density at one site and reduced flowering-tuft density and flowering-tuft inflorescence number at two sites. Suppression was limited to the year of application only. Terbacil followed by (fb) foramsulfuron (35 g ai ha –1 ) did not improve suppression. A terbacil tank mixture with glufosinate (750 ai ha –1 ), however, reduced flowering-tuft density and flowering-tuft inflorescence number at each site and reduced total tuft density at one site, suggesting improved suppression with terbacil + glufosinate relative to terbacil alone. Terbacil + glufosinate fb foramsulfuron gave additional reductions in total tuft density at two sites and reduced bearing-year flowering-tuft density at two sites, indicating that hair fescue suppression with this herbicide combination extends into the bearing year. Although less effective than the industry standard pronamide applications, terbacil + glufosinate or terbacil + glufosinate fb foramsulfuron could be used as part of a weed management program for 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 | 0.000 | 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".