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Solid-State Lighting: Health Effects and Knowledge Gaps

2024· article· en· W4407691415 on OpenAlexafffund
Christophe Martinsons, Jennifer A. Veitch, Sarah Loughran, Ashley Nixon, Rohan Mate, Rachael Harris, Linda Shen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicImpact of Light on Environment and Health
Canadian institutionsNational Research Council Canada
FundersNatural Resources CanadaAgence de la transition écologique
KeywordsSolid-state lightingComputer scienceState (computer science)OptoelectronicsMaterials scienceLight-emitting diode

Abstract

fetched live from OpenAlex

The health effects of LED lamps and luminaires used for general lighting were reviewed by an international team of researchers working under the 4E implementing agreement of the International Energy Agency (SSLC platform). Effects on the general population and subsets of sensitive groups were studied considering interactions with eye and skin, glare, circadian regulation, neuro-behavioral effects, effects of temporal light modulation, and long-term effects, such as age-related macular degeneration, myopia, and risks of cancer. This paper presents the conclusions and recommendations of the expert group. Knowledge gaps are detailed in each effect category.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
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)0.0010.003

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 head0.010
GPT teacher head0.316
Teacher spread0.306 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations2
Published2024
Admission routes2
Has abstractyes

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