Natural Revegetation of a Boreal Gold Mine Tailings Pond
Bibliographic record
Abstract
Abstract Understanding the natural revegetation of forests disturbed by the dumping of mine wastes is vital for the success of reclamation strategies. The Gunnar gold mine tailings pond in southeast Manitoba has remained largely unvegetated since the mine was closed in 1942, with limited vegetation developed on one side of the pond. We examined the natural Picea mariana/Larix laricina forest that has developed on the pond to determine how the plant community develops and what changes in the tailings are associated with this development. Vegetation sampled along transects showed a consistent pattern of succession from Equisetum palustre to Salix spp., and Populus balsamifera, to Larix laricina and finally to P. mariana. Larix laricina and P. mariana are moving into the site at the rate of 1.5 m per year with L. laricina invading 4 years ahead of P. mariana. Both tree species show a similar pattern of annual growth, showing positive correlations with spring precipitation, a pattern also occurring on L. laricina growing on a nearby site. The establishment of E. palustre was accompanied by initially rapid decreases in compaction and conductivity of the tailings, and an increase in inorganic nitrogen. Surface organic matter depth, coarse organic matter mass, and soil organic carbon increased at a constant rate, whereas subsurface coarse organic matter had an initial rapid increase followed by a gradual increase. As fern allies (and specifically members of the Equisetaceae family) have a number of properties that facilitate succession on mine wastes, their use should be explored further.
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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) | 0.000 | 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".