Establishment of Wild Roses for Commercial Rose Hip Production in Atlantic Canada
Bibliographic record
Abstract
Rose species of the genus Rosa are found growing wild throughout the Atlantic Provinces of Canada in a multitude of different habitats. Rose hips, the marketable product from these rose species, are a rich natural source of bioactive compounds useful in the pharmaceutical industry. In 2004, a wild rose field experiment was established using planting stock propagated from numerous wild rose (Rosa virginiana ×carolina) isolate accessions collected from populations throughout Prince Edward Island. The objective of this study was to investigate the effects of several field management practices on the establishment of a commercial rose hip plantation in Atlantic Canada. Treatments were applied at planting and included three in-row mulch (none, bark, and straw) treatments, three in-row fertility (none, compost, and fertilizer) treatments, and two interrow management (tilled and sod) treatments. Mulching increased nutrient uptake of N and P and increased plant growth. Fertilizer increased plant growth and yield of rose hips compared to no fertilizer or compost treatments. Tilled interrow treatment increased in shoot lengths, diameters, and plant spreads compared to interrow sod. This study indicates that during the early establishment years of a rose hip plantation, wild roses grow best with the use of mulch, fertilizer, and tillage between the rows.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".