Native Seedling Colonization on Stockpiled Mine Soils Is Constrained by Site Conditions and Competition with Exotic Species
Bibliographic record
Abstract
Sites disturbed through mining practices can be challenging to restore with native vegetation, as the path of ecological succession is often unknown and hard to predict. We conducted an establishment study that explored restoration strategies to increase native vegetation on a newly formed stockpile of soil at New Gold’s New Afton Mine in British Columbia, Canada. Establishment of native species in semi-arid grasslands is often constrained by seed and seedling microsite limitations, so treatments were imposed to assist in reducing these limitations. We established a fully factorial design with a native seed treatment (seeded and unseeded) and four soil preparations: raking, hydroseed slurry, a combination of raking and hydroseed slurry, and no preparation. Raking assisted in increasing total seedling establishment, regardless of whether the site was seeded or not. Raking and seeding increased the number of native seedlings and resulted in the greatest species richness, suggesting that native seedling establishment is primarily seed-limited, but that microclimate is also important for the establishment of some native seeds. We found that exotic species were able to capitalize on the disturbance and outcompeted the native species, but reducing seed-limitations by sowing more native seeds and increasing available microclimates by raking or tilling may increase native species’ success at the establishment phase.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".