Fall-bearing Year Herbicides and Spring-nonbearing Year Foramsulfuron Applications for Hair Fescue Management in Lowbush Blueberry
Bibliographic record
Abstract
Hair fescue ( Festuca filiformis ) is a tuft-forming perennial grass that reduces yields in lowbush blueberry ( Vaccinium angustifolium ) fields. Nonbearing year foramsulfuron applications suppress hair fescue, but there is interest in increasing suppression through foramsulfuron use in conjunction with fall-applied herbicides. The objective of this research was to determine the main and interactive effects of fall-bearing year herbicide applications and spring-nonbearing year foramsulfuron applications on hair fescue. The experiment was a 5 × 2 factorial arrangement of fall-bearing year herbicide (none, terbacil, pronamide, glufosinate, dichlobenil) and spring-nonbearing year foramsulfuron application (0, 35 g·ha −1 ) arranged in a randomized complete block design at lowbush blueberry fields in Portapique and Stewiacke, Nova Scotia, Canada. Spring-nonbearing year foramsulfuron applications did not reduce total tuft density or consistently reduce flowering tuft density, flowering tuft inflorescence number, or flowering tuft seed production. Fall-bearing year pronamide applications reduced hair fescue density for the 2-year production cycle, although additional bearing year density reductions occurred when pronamide was followed by spring-nonbearing year foramsulfuron applications at Stewiacke. Fall-bearing year dichlobenil applications reduced total and flowering tuft density at each site, although reductions in flowering tuft inflorescence number and seed production were most consistent when followed by spring-nonbearing year foramsulfuron applications at Stewiacke. Suppression extended into the bearing year at each site, and dichlobenil should be examined further for hair fescue control. Fall-bearing year glufosinate applications reduced hair fescue total tuft density at each site and flowering tuft density and flowering tuft seed production at Stewiacke. Fall-bearing year glufosinate applications followed by spring-nonbearing year foramsulfuron applications also reduced nonbearing year flowering tuft inflorescence number and bearing year hair fescue seedling density at Stewiacke, indicating that this treatment may reduce hair fescue seedling recruitment at some sites. Fall-bearing year terbacil applications did not suppress hair fescue and are not recommended for hair fescue management 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) | 0.000 | 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".