Restoration of<i>Hydrastis canadensis</i>by Transplanting with Disturbance Simulation: Results of One Growing Season
Bibliographic record
Abstract
Abstract Having evolved in an environment with large, severe, and frequent disturbances, including massive floods, fires, and impacts of extinct and extirpated fauna, woodland herbs may be adapted to such disturbance processes. Present lack of such disturbances may contribute to present rarity. We test the hypothesis that transplanting with disturbance simulation can be used to restore the threatened woodland herb, Hydrastis canadensis (goldenseal). Three disturbance‐simulation treatments (soil turnover, fertilization, and both) and a control were randomly applied to 100 blocks in goldenseal habitat, and a single rhizome was transplanted into each treatment. Transplanting was effective with 85% of the transplants surviving, 41% flowering, and 34% fruiting; thus, transplanting may increase area of occupancy. Soil turnover alone and combined with fertilization caused a significant increase in plant size available to support production of fruit. Increased flower and significantly increased fruit production were also characteristic of soil‐turned plots. Results support the hypothesis that some woodland herbs are rare due to lack of certain disturbance, call for consideration of soil disturbance as a potentially important and beneficial influence on woodland herbs regardless of light effects, and suggest that transplanting into soil‐overturned plots may restore goldenseal. The assumption that undisturbed conditions are optimal may impede effective management of rare woodland flora, highlighting the need for a more flexible approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".