Fertilization stimulate root production in cloudberry rhizomes transplanted in a cutover peatland
Bibliographic record
Abstract
Cloudberry has good economic potential for Canada, but crop practices must be improved before commercial production can be established. Transplants usually consist of rhizome segments collected in natural populations; however, the very low root density of these transplants might partly explain their initial slow growth and high mortality. The objective of this study was to determine the effects of mineral fertilization and auxin applications on root initiation and elongation. Three N–P–K fertilization treatments were applied at the planting of bare rhizomes in peatlands, while auxin applications were tested in both greenhouse and field experiments. Roots of fertilized plants were two to four times longer and more numerous than those of control plants after one complete growing season but fertilization did not lead to early rooting. Rhizome segments produced new shoots before investing in root production, suggesting that rhizome carbohydrate reserves are not sufficient to allow both the shoot and root to be produced at the same time. Auxin applications to the rhizomes incurred high mortality and did not stimulate root production in both the field and greenhouse experiments. We conclude that fertilizers applied at planting can improve cloudberry initial survival rate, rooting, and early shoot growth, which could eventually lead to improved plant cover and fruit yield.
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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 | 0.000 | 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".