Bibliographic record
Abstract
When wild blueberries were first selected and cultivated in the early 1900’s, farming practices were largely organic in nature. Early farmers established effective cultural practices, initiated mechanical weed management and took advantage of naturally occurring biological controls. To bolster these proven practices for modern production, the Rutgers Blueberry Working Group has investigated several additional methods. Other land grant universities have recently begun similar advances in applied research. Key examples include:•Weed Management – weed suppression between rows with plantings of fescue cultivars•Weed Management – weed suppression within rows with landscape fabric and mulch•Soil Biology – organic compost from various sources within the planting trench•Water Management – trickle irrigation to minimize leaf wetness, diseases and insects •Disease Resistance – cultivar comparisons of disease susceptibility •Organic fungicides – OMRI approved materials for botrytis and other pathogens •Organic insecticides – Entrust-spinosad formulations for blueberry maggot and other pests •IPM systems - pheromone trapping, monitoring and scouting •Nutritional analysis of blueberry fruit (cultivar Bluecrop)Results from small plot and grower demonstrations with various organic approaches led to a more science-based system focused on pest problems, phenological factors and soil health. Commercial acreage increases on the east coast, west coast, Canada, South America, Europe and Africa demonstrated adoption of these practices as organic blueberry production steadily increases to meet market demand.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".