Transplanting Following Non-Native Plant Control in Rocky Mountain Foothills Fescue Grassland Restoration
Bibliographic record
Abstract
Disturbed areas within national parks, resulting from historic and current land use activities, can harbor large and diverse populations of non-native plant species. These species must be controlled to prevent their spread and to restore native grassland. Research on alternative revegetation methods is urgently needed since seed based grassland restoration is often unsuccessful. The effectiveness of transplanting to restore foothills fescue grassland following implementation of non-native plant management was investigated at three disturbed sites in Waterton Lakes National Park, Alberta, Canada. Non-native plant abundance was reduced by cutting, glyphosate application, and steaming. Greenhouse grown seedlings of native fescue grassland species (13 forbs, five grasses, two shrubs) were outplanted into plots following management treatments, as three different sizes of container stock. The effects of prior non-native plant management and container size on transplant survival and growth were assessed over two growing seasons. Rhizomatous forbs and bunchgrasses with well-developed root systems had highest survival (> 50% over 14 months). Transplants with 10- to 15-cm rooting depth in cones and root trainers had significantly higher within-year survival than tray transplants with a shallow rooting depth. Transplant survival was improved by glyphosate application to control non-native plants prior to planting. Transplanting was effective for increasing native cover and species richness although high winter mortality reduced this effect. Key species for fescue grassland function were introduced at all sites and persisted for two growing seasons.
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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) | 0.000 | 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 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".