Effect of Planting Season, Bunchgrass Species, and Neighbor Control on the Success of Transplants for Grassland Restoration
Bibliographic record
Abstract
Abstract Constraints to grassland and open forest restoration (e.g., poor seed sources, yearly variation in establishment, and the persistence of weeds) necessitate the development of innovative methods to restore bunchgrass communities. We assessed the use of two native bunchgrass transplants, Bluebunch wheatgrass (Pseudoroegneria spicata) and Spreading needlegrass (Achnatherum richardsonii), for restoration within thinned montane forest communities of southeastern British Columbia, Canada. Fall and spring plantings were examined, either with or without glyphosate treatments to Pinegrass (Calamagrostis rubescens) neighbors.Calamagrostis rubescensis abundant in grassland affected by tree encroachment and may limit transplant establishment. Bunchgrass survival was positively associated (p< 0.05) with transplant size. AlthoughP. spicatasurvival was greater (p< 0.01) with fall (81%) than with spring (44%) planting, survival ofA. richardsoniiwas greater (p< 0.01) when planted in the spring (68 vs. 23%). Reduction ofC. rubescensled to a relatively small but significant increase (p< 0.05) in bunchgrass survival by 7%. The summer after planting, changes in transplant tiller number varied by bunchgrass species, planting season, and treatment of neighboringC. rubescens. Removal of neighboringC. rubescensgenerally increased the number of tillers (or reduced tiller loss) but only within fall‐plantedA. richardsoniiand spring‐plantedP. spicata. BothA. richardsoniiandP. spicatatransplants have potential for understory restoration within thinned montane forests, particularly using larger individuals, although to maximize survival, these species should be planted in the spring and fall, respectively. Reduction ofC. rubescensmay also enhance transplant survival and in some cases growth.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".