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Record W2898330757 · doi:10.2105/ajph.2018.304700

Shade as an Environmental Design Tool for Skin Cancer Prevention

2018· review· en· W2898330757 on OpenAlexfundno aff
Dawn M. Holman, George Thomas Kapelos, Meredith L. Shoemaker, Meg Watson

Bibliographic record

VenueAmerican Journal of Public Health · 2018
Typereview
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
FundersCancer Council VictoriaFaculty of Engineering and Architectural Science, Ryerson UniversityOak Ridge Institute for Science and EducationCenters for Disease Control and PreventionColorado Department of Public Health and EnvironmentWorld Health OrganizationU.S. Department of Energy
KeywordsContext (archaeology)AuditArchitectural engineeringWork (physics)Environmental planningBusinessEnvironmental healthGeographyMedicineEngineering

Abstract

fetched live from OpenAlex

Little work has been done to explore the use of shade for skin cancer prevention in the context of the built environment. In an effort to address this gap and draw attention to the intersection between architectural and public health practice, we reviewed research on shade design, use, and policies published from January 1, 1996, through December 31, 2017. Our findings indicate that various features influence the sun-protective effects of shade, including the materials, size, shape, and position of the shade structure; the characteristics of the surrounding area; and weather conditions. Limited research suggests that shade provision in outdoor spaces may increase shade use. Shade audit and design tools are available to inform shade planning efforts. Shade policies to date have mostly been setting specific, and information on the implementation and effects of such policies is limited. Integrating shade planning into community design, planning, and architecture may have a substantial impact and will require a multidisciplinary approach.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.114
GPT teacher head0.400
Teacher spread0.287 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations45
Published2018
Admission routes1
Has abstractyes

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