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Record W4406458576 · doi:10.1371/journal.pstr.0000156

Impact of green wall orientation on building energy performance in a tropical climate: An experimental assessment

2025· article· en· W4406458576 on OpenAlexaff
V. M. Jayasooriya, Chathuri Tharanga Liyanage, Shobha Muthukumaran, Rathmalgodage Thejani Nilusha

Bibliographic record

VenuePLOS Sustainability and Transformation · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsUniversity of SaskatchewanUniversity of Toronto
Fundersnot available
KeywordsOrientation (vector space)Tropical climateEnergy (signal processing)Environmental scienceGeographyArchitectural engineeringGeometryEngineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

Vertical Greenery Systems, also known as Green Walls have emerged as essential components of Green Infrastructure, offering promising outcomes for both the present and the distant future. This study aimed to establish correlations between orientation and the thermal performance of green walls. The research was conducted in a controlled climatic environment, featuring a bare wall as the control experiment and green walls with four different plant species, including Xiphidium caeruleum , Asparagus aethiopicus , Ophiopogon japonicas , and Dianella ensifolia variegate. The growth medium consisted of a consistent 1:1:1 ratio of coir dust, sand, and compost for all plant species. Data collection, which spanned from 9:00 a.m. to 6:00 p.m., included parameters: surface temperature, ambient temperature, relative humidity, and wind speed for the green wall’s exterior and interior. The results of the study demonstrated that east-oriented green walls, particularly those featuring Asparagus aethiopicus , achieved significant influence on building energy conservation, with a maximum temperature reduction of 4.1 °C in both interior and exterior surface temperatures compared to the bare wall. The findings highlight the potential of optimally oriented green walls to reduce cooling energy consumption in buildings.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.349

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.282
Teacher spread0.275 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2025
Admission routes1
Has abstractyes

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