Grassland reclamation of a copper mine tailings facility: Long term effects of biosolids on plant community responses
Bibliographic record
Abstract
Aim: We explore long-term plant community responses 17 years after a one-time application of biosolids (0, 50, 100, 150, 200, 250 dry Mg∙ha-1) to determine (1) whether the land application of biosolids on mine tailings, seeded with agronomic grass-legume mixture, affects long-term plant community responses, (2) how application rates and soil texture influenced plant community responses and community structure, and (3) whether native plant species have colonized and contributed to the reclaimed plant community. Location: Two tailings deposits (sand and silt loam) generated by a copper-molybdenum (Cu-Mo) mine in Southern British Columbia, Canada. Methods: Plant communities were sampled by visual evaluation of percent cover to the lowest taxonomical level possible. Vegetation surveys were completed on two mine tailings deposits within the storage facility that have different soil textures (sand and silt loam). Results: Results showed that the interaction of biosolids applications and soil texture impacted multiple community plant responses, including increasing plant cover at both sites, and increasing richness, evenness and diversity at the sandy site. Biosolids application enhanced the performance of spontaneously established species (volunteer species) and non-native/naturalized grasses. Conclusion: Our study demonstrated that biosolids application facilitates ecological succession by enhancing the establishment of non-native volunteer species over the long-term, which increases vegetative cover on both deposits and promotes plant communities diversity on sites with sandy soil texture.
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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 | 0.000 | 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".