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Record W4242896023 · doi:10.32920/ryerson.14657580

The Potential for Perennial Vines to Mitigate Summer Warming of an Urban Microclimate

2021· preprint· en· W4242896023 on OpenAlexaffabout
Michelle Blake

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsMcMaster UniversityToronto Metropolitan University
Fundersnot available
KeywordsMicroclimateShadingEnvironmental scienceUrban heat islandAtmospheric sciencesVegetation (pathology)VineAir temperatureImpervious surfaceGlobal warmingMeteorologyClimatologyGeographyClimate changeEcology

Abstract

fetched live from OpenAlex

Shading and evapotranspirative cooling by vegetation are important controls on moderating rise in city temperatures and mitigating urban heat islands. The purpose of this research is to evaluate the potential of Boston ivy (Parthenocissus tricuspidata) to mitigate warming of building surface temperature in an urban core. Temperature loggers were placed on vine-shaded and non-shaded walls in Toronto, Canada to collect surface temperatures over a six-month period. During peak solar access periods, average vine-shaded and non-shaded temperature differentials of up to 6.5 °C and 7.0 °C for the south and west-facing walls were measured, respectively. Predictive models were developed to estimate daily degree hour difference (DHD), a metric for capturing the temperature moderating potential of vines. At ambient air temperatures exceeding 22 °C, ambient air temperature and solar radiation were significant positive drivers of DHD. Results are important to further understanding urban plant-microclimate interactions and strategies for heat island management.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.255
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2021
Admission routes2
Has abstractyes

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