The effects of agronomic herbaceous plants on the floristic composition at an early successional stage on gold mine tailings
Bibliographic record
Abstract
Agronomic herbaceous plants are used in mine tailing revegetation, yet research is limited on how different compositions facilitate forest restortion. This study evaluated the effect of various agronomic herbaceous plant treatments (grasses, legumes, or a mix) on plant diversity and the establishment of early-successional boreal forest tree species at a gold mine site in Québec, Canada. In 2013, an experimental area was divided into three blocks, each with five plots randomly seeded with one of the following: 100% grass, 100% legumes, a mixture of both, topsoil, or control (no seeding). Plots were further subdivided to assess volunteer plant colonization and diversity. Results indicated that species richness was significantly higher in the topsoil treatment compared to the agronomic herbaceous treatments and control. However, no significant differences were found among the agronomic treatments. The Shannon diversity index was higher in the topsoil compared to the legume treatment. Species composition varied significantly between treatments, with topsoil dominated by introduced species. The legume treatment supported more volunteer pioneer trees, particularly of the Salicaceae family, than the grass or mixed treatments, suggesting that legumes may better promote deciduous pioneer tree establishment during revegetation of mine tailing.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".