Managing contingency in semiarid grassland restoration through repeated planting
Bibliographic record
Abstract
The success of grassland restoration in semiarid regions is contingent on good establishment years. Here it is asked whether success rate can be increased by planting repeatedly among years, or by consistently using high planting densities. I planted seeds of a dominant native perennial grass, Elymus lanceolatus, at five densities (range: 30–3000 seeds/m2) in each of 3 years. Elymus seedling emergence and cover increased significantly with planting density. Cover after three growing seasons was maximized (i.e. not significantly different from the greatest cover) at planting densities of 300–600 seeds/m2. On the other hand, low germination in a dry warm year resulted in density having no effect on cover 3 years later. Two favorable planting years showed trajectories to native dominance after three growing seasons. In contrast, one unfavorable planting year showed a trajectory of non‐native dominance. Emergence was increased modestly by both herbivore and interspecific neighbor removal. Overall, the most economical process for ensuring success (assuming that seed costs are high and planting and site‐preparation costs are low) may be to plant at moderate density (300–600 seeds/m2) in repeated years until a favorable year is encountered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".