From the ground up: greening brownfields in our community
Bibliographic record
Abstract
Vancouver currently has an estimated 4000 brownfields, and this number increases dramatically when the context is stretched to include Canada on a whole. Our research team proposes that phytoremediation, the in-situ remediation of contaminants from brownfields, be adopted as a strategy for revitalizing brownfield sites within the City of Vancouver. With urban renewal and revitalization on the increase, these lands require reclamation rather than drawing from existing greenspaces that are currently in use for food production. Left undeveloped, brownfields have little positive economic value and remain an eyesore within the community. Contamination may be real or perceived but the potential for harm to human health is real. This proposal expands on research conducted that studied the potential for utilising native plants in phytoremediation projects. A literature review was conducted and the PHYTOREM © and BC Plant Species databases were cross-referenced. Notwithstanding the lack of this application on a smaller urban scale it is the opinion of the research team, that field studies be entertained at the expense of the polluter and that potential sites exist within the Cedar Cottage/Kensington neighbourhood in Vancouver.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".