Are newly available soil amendments helpful to the 50 years of practices in restoring woody landscapes in Sudbury, Ontario, Canada?
Bibliographic record
Abstract
Sudbury has been a producer of base metals, especially nickel and copper, for over 140 years. Decades of atmospheric sulphur and metal pollution resulted in a sparse plant cover and stunted trees that led to severe erosion and degradation of forest soils. However, since the 1970s, pollution controls and the outstanding Sudbury Regreening Program have rehabilitated 25,000 ha of impacted landscape. Increasingly, the program is focusing on restoring native biodiversity (utilising 75 native trees and shrubs) and introducing understory species. A diversity of lichens and mosses has also returned to the developing forests and soil microbe communities are re-establishing. Estimates of forest carbon stocks in the regreened upland landscapes of Sudbury since 1978 show about 0.67 M tonnes of sequestered carbon or the equivalent of ca. 10 years of fossil fuel carbon emissions from the region. Other soil amendments as potential replacements for the limestone and fertilisers currently used in the Regreening Program are under investigation in short-term experiments. These included residuals from pulp and paper mills (wastewater treatment biosolids, biomass boiler ash) and municipal wastewater treatment biosolids, all showing some potential benefits. Overall, the landscape around Sudbury has greatly changed in the past 50 years enough that no more intervention is needed in some areas. The landscape changes have given a new image of the city and provided opportunities for recreation and other outdoor activities. There is a current effort to restore some of the damaged peatlands within the city.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".