An Overview of Weed Management in the Wild Lowbush Blueberry—Past and Present
Bibliographic record
Abstract
SUMMARY The wild lowbush blueberry (Vaccinium angustifolium Ait.) is an important successional species of cleared woodland and abandoned farmland of northeastern North America where commercial, managed blueberry fields have been developed. Unlike other fruit crops, the weed flora is unique and consists mainly of a broad range of native herbaceous and woody perennial species that thrive under the two-year cropping system. Traditionally, weedy vegetation was controlled or suppressed by burning, cutting, and roguing, and regenerating woody and herbaceous species were the major weed problems. The introduction of phenoxyalkanoic herbicides in the late 1940s lead to the early development by innovative growers of selective roller/wiper applicators that could control the taller, weedy overstory. Several selective preemergence herbicides (terbacil and diuron) were introduced in the 1970s to control grasses and some broadleaved weeds, and hexazinone was approved in Canada in 1982 and in Maine in 1983. This soil-applied, broad spectrum herbicide has controlled many of the common woody and herbaceous weeds. Its widespread use lead rapidly to increased yields and, directly or indirectly, it has contributed to changes in other production practices, such as the further development of mechanical harvesters and increased fertilizer use. However, the almost total reliance on the repeated use of hexazinone has introduced other problems, including shifts in weed species, the development of resistance, and soil degradation on vegetation-free soils. The highly soluble nature of the herbicide has resulted in wide-spread detection of hexazinone in groundwater adjacent to managed blueberry fields. Best Management Practices have been introduced to minimize problems associated with hexazinone use and is leading to new approaches to vegetation management that employ reduced risk herbicides, lower rates, mulches and ground covers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".