The Red + Green: creating a regenerative narrative through the industrial wastelands of Sudbury, Ontario
Bibliographic record
Abstract
Sudbury, Ontario, is recognized for its miraculous late \ntwentieth-century Regreening efforts to remediate the \nsmelter-polluted ‘moonscape.’ Yet, given the continued \nextractive activity and unmanaged mine waste, some areas \nremain subject to extensive environmental degradation. \nTherefore, a critical reflection on these industrial practices is \nnecessary to continue this incomplete regenerative narrative, \nfully restoring all parts of the land. Thus, this thesis is informed \nby research into innovative ground surface treatment and \nbiotechnologies to treat mine waste, and in regenerative \narchitectural design principles. It demonstrates the potential \nof lifting the veil on hidden industrial wastelands, rehabilitating \nCopper Cliff’s Central Tailings Area into a thriving regenerative \npark, and envisioning an interpretive centre into a mine waste \nfacility that is harmoniously integrated into the changing \nlandscape. This contributes to landscape remediation and \nintegrates place-based storytelling to educate and empower \nfuture generations to participate in land stewardship and create \nongoing sustainable environmental and social impacts.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".