The management of brownfields in Ontario: A comprehensive review of remediation and reuse characteristics, trends, and outcomes, 2004–2015
Bibliographic record
Abstract
Brownfields remediation and redevelopment continues to be an important issue for policy makers and planners seeking to unlock its many socio-economic and environmental benefits. While technical approaches to assessment and remediation have become rather standardized and governments have largely embraced voluntary programs to oversee their application, the degree of regulatory oversight continues to differ among jurisdictions. This article examines the scale and character of remediation activity in Ontario, Canada over the last decade using records submitted by qualified persons from the private sector. It finds that Ontario’s approach has been quite successful in scale and character in stronger urban real estate markets despite most matters related to cleanup and reuse escaping the direct oversight of provincial regulators. The province’s less-interventionist approach may need some review to address the nature of cleanup techniques being applied and the recent slowdown in cleanup and reuse activity, especially given the growing push toward regional growth management and more effective use of brownfield land resources in both larger urban areas, and smaller ones where greenfields are plentiful and brownfields are less competitive.
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.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".