Cloudberry Cultivation in Cutover Peatlands: Hydrological and Soil Physical Impacts on the Growth of Different Clones and Cultivars
Bibliographic record
Abstract
Cloudberry ( Rubus chamaemorus L.) cultivation is receiving increasing attention as a means of revitalising regional economy and rehabilitating cutover peatlands. The study reported here investigated the necessary soil physical and hydrological conditions, the compatibility of cloudberry cultivation with restoration of mined peatlands, and the performance of newly commercialised Norwegian cultivars in North America. Terraces at two levels were landscaped in peatland after vacuum extraction of peat to create different growing conditions in terms of hydrology and soil properties, then planted with two Norwegian cultivars (Fjordgull and Fjellgull) and two local (east Canadian) clones of cloudberry in a randomised block experiment. After three years, both the clones and the cultivars grown on the lower terrace had more leaves per m2 due to lower soil bulk density combined with higher average water level. Mulching, inherent to restoration, reduced the number of leaves produced during the year following planting. The Fjordgull cultivar had a higher survival rate than Fjellgull and local clones. Overall, the number of living rhizomes decreased over the years following planting. These results suggest that soil properties (bulk density and porosity) significantly influence cloudberry establishment and growth. Rhizomes should be planted two or three years after peatland restoration to avoid the initial negative effects of the mulch.
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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 | 0.001 |
| 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".