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The Relationship Between Stress and Greenspace Exposure

2024· article· en· W4401183854 on OpenAlexaff
Leyi Zhao

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWoodlandStress (linguistics)Natural (archaeology)Physiological stressPsychologyGeographyEcologyMedicine

Abstract

fetched live from OpenAlex

This essay use literature review to explores the relationship between greenspace and stress, drawing on evidence from multiple studies that utilize cortisol measurements to indicate changes in stress levels. Greenspace refers to all natural green areas, including parks, woodlands, scrublands, and other natural environments. People's stress can be influenced by various factors such as the environment, family, friends, and work or study. Therefore, it is important to understand how much a green environment can reduce stress. The findings strongly suggest that greenspace has a beneficial impact on reducing mental stress. However, the influence of greenspace exposure varies by gender. For men, a moderate amount of greenspace is optimal for stress recovery, while more greenspace does not necessarily lead to greater improvement. In contrast, the effects on women require further investigation to be fully understood.

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 score0.125
Threshold uncertainty score0.416

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)0.0000.000

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 head0.036
GPT teacher head0.336
Teacher spread0.300 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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