Cloudberry cultivation in cutover peatland: Improved growth on less decomposed peat
Bibliographic record
Abstract
Bussières, J., Rochefort, L. and Lapointe, L. 2015. Cloudberry cultivation in cutover peatland: Improved growth on less decomposed peat. Can. J. Plant Sci. 95: 479–489. Cloudberry cultivation is being seriously considered as a rehabilitation option for industrial peatlands after horticultural peat extraction has ceased. Besides increasing the ecological and economic values of these sites, cloudberry cultivation could improve fruit yield and facilitate fruit harvesting compared to picking in natural peatlands. Previous studies reported slow establishment that was tentatively associated with substrate characteristics. Field and greenhouse experiments were thus conducted to better characterize the impact of different peat substrates in combination with restoration techniques on the growth of male and female clones. Cloudberry grew much better in less-decomposed fibric peat (H1–H3) than in more-decomposed mesic peat. Restoring the moss layer of the former peat field would thus need to precede cloudberry planting by a few years, in order to plant the rhizomes in a newly formed fibric peat layer. Male clones produced larger leaves and more ramets per rhizome than female clones under common greenhouse conditions, which indicated that differences between sexes are most likely genetic rather than environmental. Furthermore, we found cloudberry clones may be very sensitive to aluminium toxicity. In conclusion, the degree of peat decomposition appears to be one of the key factors determining the success of cloudberry plantations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".