How big is the footprint? Quantifying offsite effects of mines on boreal plant communities
Bibliographic record
Abstract
Threats from mining to the biodiversity and ecological services of boreal forests are increasing as demand for minerals increases globally. However, much less is known about how offsite effects of mines affect understory communities as they occur outside the immediate location of mining and are often overlooked during ecological evaluations. We conducted an extensive field survey along 1-km transects surrounding six mine sites of different mining stages (operating vs non-operating), that crossed four ecosystem types (deciduous, coniferous, mixed forests and open canopy) in Canada’s boreal forest. The offsite effects of mines on the understory were quantified using vascular plants (woody and herbaceous), bryophytes and lichens. Mine offsite effects impacted understory diversity and composition. Understory richness and cover was more reduced near operating than non-operating mines. Mining stage mainly significantly altered understory diversity and community structure in deciduous and mixed forests while understory communities were more resistant to the offsite effects in coniferous forest (P > 0.05). The footprint was quantified using the influenced distance and the strongest effects were generally within 0.2 km from mines. Given the predicted changes in boreal forest ecosystems with encroachment of deciduous species into coniferous forests and the increased sensitivity of mixed and deciduous forests, the area affected by offsite effects of mines could grow in the future. We suggest that offsite effects should be included in ecological evaluations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".