Effects of fall bearing-year glufosinate applications, spring nonbearing-year glufosinate applications, and spring nonbearing-year foramsulfuron applications on hair fescue (<i>Festuca filiformis</i>) in lowbush blueberry
Bibliographic record
Abstract
Abstract Hair fescue is a common perennial grass that reduces yields in lowbush blueberry fields. This grass is suppressed with nonbearing-year foramsulfuron applications, though suppression may be improved through use of sequential glufosinate and foramsulfuron applications. The objective of this research was to determine the main and interactive effects of fall bearing-year glufosinate applications, spring nonbearing-year glufosinate applications, and spring nonbearing-year foramsulfuron applications on hair fescue. The experiment was a 2 by 2 by 2 factorial arrangement of fall bearing-year glufosinate application (0, 750 g ai ha –1 ), spring nonbearing-year glufosinate application (0, 750 g ai ha –1 ), and spring nonbearing-year foramsulfuron application (0, 35 g ai ha –1 ) arranged in a randomized complete block design at lowbush blueberry fields located in Parrsboro and Portapique, NS, Canada. Fall bearing-year glufosinate applications, spring nonbearing-year glufosinate applications, and spring nonbearing-year foramsulfuron applications alone provided inconsistent hair fescue suppression. Fall bearing-year glufosinate applications followed by spring nonbearing-year foramsulfuron applications, however, reduced nonbearing-year total tuft density, flowering-tuft density, and flowering-tuft inflorescence number at each site and reduced seed production at Portapique. Sequential fall bearing-year and spring nonbearing-year glufosinate applications or sequential spring nonbearing-year glufosinate and foramsulfuron applications reduced flowering-tuft density and flowering-tuft inflorescence number at each site but did not consistently reduce total tuft density. Sequential herbicide treatments reduced bearing-year seedling density and may therefore contribute to hair fescue seed bank management 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".