Impacts of experimental leaf harvesting on a North American medicinal shrub, <i>Rhododendron groenlandicum</i>
Bibliographic record
Abstract
Harvesting of medicinal plants from wild populations is increasing worldwide, however, studies on sustainable harvesting techniques are lacking. In this exploratory study, we investigated the impact of leaf harvesting on Rhododendron groenlandicum (Oeder) Kron & Judd, a North American temperate shrub, used traditionally as a medicinal plant by the Cree Nation. The species is widely distributed, but Crees are worried that commercial harvesting could threaten local plant populations. Our study was conducted near the Cree Nation of Mistissini (James Bay, Northern Quebec). Three leaf harvest regimes were tested in 2008 and 2009: no harvest, all leaves harvested, and only old leaves harvested; each treatment was performed on 30 plants. The harvesting of all leaves had a negative impact on stem elongation after the first harvest, while leaf production and stem radial growth decreased after the second harvest. Two-thirds of the plants also died following the second regime of harvesting all leaves. The harvesting of old leaves had no significant impact on growth, leaf production, or survival of R. groenlandicum, even after 2 years of harvest. These results lead to the conclusion that sustainable harvest of this species is possible, but further study is required to make definite recommendations.
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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 | 0.001 |
| 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".