Native seed mix richness impacts revegetation of reclaimed land
Bibliographic record
Abstract
Interest in using native plant species for land reclamation and ecological restoration continues to increase. Yet sufficient knowledge of characteristics of native species for revegetation, specifically their response in field settings, continues to lag. Thus our study was conducted to determine whether the richness of native seed mixes impacted plant community development following reclamation in the Aspen Parkland ecoregion of Alberta, Canada. Four seed mixes were used: mix I with 6 grasses, mix II with 10 grasses, mix III with 6 grasses and 10 forbs, and mix IV with 10 grasses and 10 forbs. Each seed mix was designed with equal amounts of pure live seed for each species. After two growing seasons, seed mix richness generally had little or no effect on seeded species richness, individual seeded species, density, non-seeded species density, and ground cover, except for seeded forbs. Seed mixes II and III, with moderate species richness, provided the most benefit for developing species rich communities. Seeding with high species richness is more expensive, sometimes difficult to procure seed, and may not associate with communities of greater species richness, negating their need.
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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 | 0.001 | 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".