Plant Community Diversity at Two Reclaimed Mine Tailing Storage Facilities in Québec, Canada
Bibliographic record
Abstract
Mining activities generate residues during the ore concentration process. These wastes are placed into large tailing storage facilities, and upon mine closure, these tailings must be reclaimed. This study aimed to determine how different reclamation methods, involving combinations of planted boreal woody species and organic amendments application (paper mill sludge biosolids, chicken manure, and topsoil) affected plant community diversity at two tailing storage facilities in Québec, Canada. We recorded the composition of the plant communities using the percent cover of plant species within 1 m × 1 m quadrats. At the Niobec mine site, paper mill sludge mixed with topsoil enhanced total plant cover was compared with the use of topsoil only; the former amendment, however, reduced evenness (J′) and diversity (1−D) due to the increased growth of grasses and invasive forbs. At the Mont-Wright site, plots having received paper mill sludge mixed with a “Norco” treatment (a mixture of chicken manure, hay, and grass seeds) produced the highest total plant cover. The Norco treatment mixed with topsoil and the single application of topsoil and biosolids produced the highest evenness (J′) and diversity (1−D). Overall, organic amendment applications promoted vegetation cover on tailings and contributed to the colonization of diverse plant communities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".