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Record W7019382485

From the ground up: greening brownfields in our community

2008· article· en· W7019382485 on OpenAlexaboutno aff

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2008
Typearticle
Languageen
FieldArts and Humanities
Topiclinguistics and terminology studies
Canadian institutionsnot available
Fundersnot available
KeywordsBrownfieldContext (archaeology)Contaminated landHarmLand reclamationNeighbourhood (mathematics)SustainabilityGreeningWetland
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.245
Teacher spread0.149 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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