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Record W1969741537 · doi:10.3992/jgb.4.4.3

Are Green Walls as “Green” as They Look? An Introduction to the Various Technologies and Ecological Benefits of Green Walls

2009· article· en· W1969741537 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Green Building · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsWorld Wildlife Fund Canada
Fundersnot available
KeywordsGreen buildingArchitectural engineeringEcologyEngineeringBiology

Abstract

fetched live from OpenAlex

Abstract According to a United Nations forecast seventy percent of the world population will be living in cities by 2050 (UNFPA 2007). Such a major shift away from rural and naturally vegetated areas to the polluted, noisy, and crowded concrete jungle of modern cities is and will continue to be profound. We must find new and innovative ways to better integrate nature into our ever expanding cities. Green roofs and parks are one way to do this but there are substantial amounts of vertical space that for the most part have been underutilized. Green walls not only bring nature back into city life, they do so in a way that is accessible to everyone. Currently green walls are at the cutting edge of interior and architectural design trends but they are also being integrated into sustainable building design for their numerous environmental benefits. This article aims to clarify what green walls are, going into detail about the various technologies available; the pros and cons of each; and the ecological, social, and economic benefits of these living works of art.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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