Establishment of silver sagebrush in the Northern Mixed Prairie
Bibliographic record
Abstract
Interest has been expressed in using silver sagebrush (Artemisia cana Pursh ssp. cana) in restoring the Northern Mixed Prairie in Saskatchewan. The objectives of this study were to determine the effects of seedbed manipulation treatments and autumn or spring sowing on establishment of silver sagebrush on sites previously seeded to native, perennial grasses. Seeds (achenes) were sown by broadcasting at 20 pure live seeds m(-2). Seedling emergence ranged from 5 to 6% of seeds sown. Most seedlings emerged in May and June; no seedlings emerged after July or in the second year after planting. Seventy-four to 84% of emerging seedlings survived the first growing season with 96 to 98% of these seedlings establishing. On upland sites, seedling emergence (1.1 seedlings m(-2) SE +/- 0.1) and establishment (0.9 seedlings m(-2) SE +/- 0.1) were similar between autumn and spring sowing and among seedbed manipulation treatments. On lowland sites, seedling emergence (1.4 seedlings m(-2) SE+0.2) and establishment (0.8 seedlings m(-2) SE +/- 0.2) were similar between autumn and spring seeding. Density of seedlings establishing was greatest when the seedbed was tilled. Establishment of silver sagebrush appears primarily limited by low numbers of seedlings emerging, indicating very specific safe site requirements for this shrub. Drastic disturbance of the seedbed is not required to establish silver sagebrush in established stands of perennial grasses. Sowing silver sagebrush in late autumn when temperatures are consistently below 0 degrees C or in early spring immediately after snowmelt is recommended.
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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 | 0.001 |
| 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".