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Legislation Restricting Access to Indoor Tanning Throughout the World

2012· article· en· W2037667464 on OpenAlexaboutno aff
Mary T. Pawlak, Melanie R. Bui, Mahsa Amir, Diane L. Burkhardt, Alan K. Chen, Robert P. Dellavalle

Bibliographic record

VenueArchives of Dermatology · 2012
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsnot available
FundersU.S. Public Health Service
KeywordsLegislationEnvironmental healthMedicineBusinessLawPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: To compile current legislation of indoor tanning throughout the world and compare them with existing legislation found in 2003. DESIGN: Cross-sectional study. SETTING: International. PARTICIPANTS: All nations with legislation regarding access to indoor tanning found through web-based Internet search. MAIN OUTCOME MEASURES: Number of nations with legislation and changes to laws regarding access to indoor tanning since 2003. RESULTS: The number of countries with nationwide indoor tanning legislation restricting youth 18 years or younger increased from 2 countries in 2003 to 11 countries in 2011. Six states or territories in Australia restricted indoor tanning in all minors; a province and a region in Canada implemented youth tanning laws; and 8 states, in addition to 3 preexisting state laws, in the United States implemented indoor tanning legislation since 2003. CONCLUSION: Since 2003, access to indoor tanning has become increasingly restricted around the world.

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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.045
GPT teacher head0.373
Teacher spread0.329 · 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

Citations82
Published2012
Admission routes1
Has abstractyes

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