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Record W1983365857 · doi:10.1117/12.382739

<title>Validation of self-reported skin color via analysis of diffuse reflectance spectra of skin</title>

2000· article· en· W1983365857 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsMcMaster UniversityCancer Care Ontario
Fundersnot available
KeywordsReflectivityPrincipal component analysisSunlightSkin colorArtificial neural networkDiffuse reflectionDiffuse reflectance infrared fourier transformColor analysisSpectral lineOpticsComputer scienceMaterials scienceArtificial intelligencePhysicsChemistryAstronomy

Abstract

fetched live from OpenAlex

The validity of self-reported skin color was assessed by comparing the responses of a skin color survey with the external measure of diffuse reflectance spectrophotometry. Reflectance spectra of 108 subjects were measured at sites on the arm normally exposed to sunlight and sites normally unexposed to sunlight. The reflectance spectra were analyzed with a variety of discriminating algorithms, such as principal component analysis, and competitive neural networks. For subjects with light and dark skin, there was good correspondence between the survey results and groupings derived by the neural network analysis. For those people reporting medium skin color, the correspondence with the neural network groupings was poor. It was unclear if this was due to poor self-reporting or deficiencies in the spectral analysis.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.247
Teacher spread0.237 · 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