Using Landscape Context to Guide Ecological Restoration: An Approach for Pits and Quarries in Ontario
Bibliographic record
Abstract
The landscape has a dramatic effect on a site's ecological and social function. Landscape context and pattern are important considerations in ecological restoration for their effects on rehabilitation design and ecological function. In Ontario, Canada, there are more than 5,300 active aggregate mining sites, equivalent to a total area of over 70 square kilometers. Rehabilitation of inactive pits is required by law, but rehabilitation efforts rarely attempt to restore ecological function to a site, and even more rarely consider the ecological implications of landscape context. The size, spatial extent, and nonrandom distribution of aggregate extraction sites in Ontario offer opportunities to restore ecological functions through cooperative rehabilitation, where landowners and licensed aggregate extractors try to achieve better ecological outcomes. In order to illustrate how landscape context can make a meaningful contribution to rehabilitation design and ecological restoration of pit and quarry sites in Ontario and in other settings, we review methods of assessing critical aspects of landscape context, including patterns of habitats (mosaics), interpatch movements and dispersal (connectivity and permeability), and the heterogeneity of microclimates (niche diversity). We illustrate the potential of this approach with the example of the Karner blue butterfly. The described project may inform restoration approaches for other land uses and landscape contexts. ©2008 by the Board of Regents of the University of Wisconsin System.
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How this classification was reachedexpand
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".