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Record W4290993424 · doi:10.3390/f13081268

Spatiotemporal Characteristics of Public Recreational Activity in Urban Green Space under Summer Heat

2022· article· en· W4290993424 on OpenAlexaff
Ziluo Huang, Jiaying Dong, Ziru Chen, Yujie Zhao, Shanjun Huang, Weizhen Xu, Dulai Zheng, Peilin Huang, Weicong Fu

Bibliographic record

VenueForests · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRecreationUrbanizationUrban heat islandEnvironmental sciencePublic spaceUrban planningChinaUrban green spaceGeographyPublic parkHeat stressEnvironmental planningEnvironmental protectionSpace (punctuation)MeteorologyEcologyCivil engineeringAtmospheric sciencesArchitectural engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

The urbanization process has contributed to the deterioration of the urban thermal environment and increased the frequency of heat waves in summer that damage public health. Urban green space is the space for the public to escape the summer heat. The cooling effect of urban green space (UGS) can encourage outdoor activities and enhance public health. Analysis of when and how the public utilizes UGS under summer heat can serve as a guide for UGS improvements. In this study, the Hot Spring Park in Fuzhou City, China was utilized as a case study to examine the characteristics of the public recreational behaviors and their influencing factors under summer heat. Results showed the following observations: (1) Canopy density and turf coverage played key roles in regulating the thermal environment. (2) UGS can accommodate multiple summertime behaviors with considerable spatiotemporal variations. (3) In the hot summer, the frequency of recreational activities in UGS was negatively correlated with temperature. Dynamic behaviors were significantly impacted by temperature. Older and younger groups were less heat-tolerant. Based on this, we propose countermeasures and suggestions that are tailored to the needs of urban residents and their behavior characteristics for the planning and management of urban parks in the summer heat.

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 score0.005
Threshold uncertainty score0.999

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)0.0020.000

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 head0.030
GPT teacher head0.241
Teacher spread0.211 · 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

Citations19
Published2022
Admission routes1
Has abstractyes

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