Are newly available soil amendments helpful to the 50 years of practices in restoring woody landscapes in Sudbury, Ontario, Canada?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 it