Ecological restoration from space: the use of remote sensing for monitoring land reclamation in Sudbury
Bibliographic record
Abstract
The use of spatial information systems has grown over the past decade as a tool for studying ecosystems and the impacts of human activity upon them. The collection of geographic data, however, is often time consuming and expensive. Remote sensing of ecological processes offers the potential to rapidly produce spatial information over large areas. This study will examine the use of earth-observation data to map the restoration activities in the City of Greater Sudbury. Sudbury has made great progress over the past 25 years in restoring the vegetation cover that had been destroyed by the effects of mining. Reductions in smelter emissions and a reclamation effort to re-vegetate the area through a large-scale soil liming and tree-planting campaign have resulted in significant land cover change. Preliminary results show that remote sensing data can produce information on the land cover type and, on the relative health of vegetation in restored areas that are consistent with other field-based studies in this region. Further validation of these results need to be made to determine the local accuracy level that can be achieved using these methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".