Seeding and transplanting native forbs on reclamation sites in Alberta, Canada
Bibliographic record
Abstract
A growing number of industrial disturbances are reclaimed with native grass species, although use of many native forbs in reclamation is limited due to low availability, high expense, poor germination, and lack of information on how to successfully establish them. Therefore, a study was conducted to assess the performance (establishment, growth, and survival) of native forbs (10 species by seeding and 12 species by transplanting) on disturbed sites planted in different seasons (spring and fall) and subjected to different types of vegetation management (mowed and unmowed). All seeded forb species established successfully, although density of most of the native forbs was very low (<20 stems/plot). Transplanted forb species established well, with 65% of seedlings surviving two growing seasons. Establishment and survival of seeded and transplanted native forbs were associated with plant competition, where forbs established well with annual weeds and less successfully with perennial weeds. Both spring seeding and transplanting increased forb establishment; and seeding and transplanting in fall versus spring produced more seed. Mowing generally had limited effects on forbs. Of the seeded species, Vicia americana, Achillea millefolium, and Ratibida columnifera consistently had the highest densities across treatments, whereas spring transplanting increased establishment of Anemone cylindrica, Gaillardia aristata, Geum triflorum, Monarda fistulosa, and Dalea purpurea. Spring‐transplanted forbs spread over a larger area than fall‐transplanted forbs. In most cases, seeding and transplanting native forbs in the spring can be more effective than in fall to reclaim disturbed sites if land management practices are used to control perennial weeds.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".