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Record W4412685984 · doi:10.1080/24749508.2025.2529028

Impact of suburban landscape on outdoor thermal comfort in tropical savanna climate

2025· article· en· W4412685984 on OpenAlexaff
Graciela Arosemena Díaz, Ariadna Mora

Bibliographic record

VenueGeology Ecology and Landscapes · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsMcGill University
FundersSecretaría Nacional de Ciencia, Tecnología e Innovación
KeywordsTropical savanna climateTropical climateGeographyTropicsThermal comfortUrban landscapeEnvironmental scienceAgroforestryPhysical geographyEcologyEnvironmental resource managementMeteorologyEnvironmental planningArchaeologyEcosystem

Abstract

fetched live from OpenAlex

This study examines the effects of various urban land cover compositions and the ratio of canyon height to canyon width on outdoor thermal comfort (OTC) in Panama City. For this purpose, the study conducted simulations with ENVI-met on three neighborhood complexes during the hottest day of the year. The study measured air temperatures and relative humidity in the three neighborhoods to validate the simulation results. The simulations utilized the measured data to evaluate outdoor thermal comfort by analyzing the Humidex. This index describes how hot the weather feels, combining the effects of air temperature and relative humidity. Results of the linear regression indicate that at 12:00 h and 15:00 h, the Humidex values decreased as the lawn cover increased. Additionally, the study observed that higher aspect ratios led to lower Humidex values at 12:00 h and 15:00 h. The results for tree canopy cover suggest that the cooling effect decreased during the hottest hours of the day. This result is likely due to a reduction in the trees’ cooling capacity, linked to the limitation of plant evapotranspiration. These findings provide valuable guidance for planners when determining lawn coverage and aspect ratios to improve outdoor thermal comfort in tropical savanna cities.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.004
GPT teacher head0.234
Teacher spread0.230 · 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
Published2025
Admission routes1
Has abstractyes

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