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Record W2109052141 · doi:10.1016/j.ijwd.2017.02.010

What Ages Hair?

2017· article· en· W2109052141 on OpenAlexaff
Assaf Monselise, David E. Cohen, Rita Wanser, Jerry Shapiro

Bibliographic record

VenueInternational Journal of Women’s Dermatology · 2017
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHair lossVitalityVirilityBody hairBlack hairAestheticsMale gazeBeautyKisspeptinPsychologyArtHistoryDermatologyMedicineBiologyPsychoanalysisMasculinityNeuroscienceAnatomy

Abstract

fetched live from OpenAlex

Hair has always played an important role in the history of mankind. Egyptian hieroglyphics are testimony of the paramount importance of hair for this ancient civilization, not only because of the visual effect, but also because of the erotic symbolism connected with hair. For the ancient Romans hair was not only a question of fashion but was used as a symbol of beauty, virility, class and intellect. In modern western culture having a full head of hair is often associated with desirable qualities such as youthfulness and vitality. Most people experience changes in hair and scalp health as they age. Subsequent hair loss may cause significant distress that deeply affects people’s life causing social anxiety and interfering with their well being. Genetic and hormonal changesareimportantfactorsinhairloss, butweatheringandgrooming habits take a toll on our hair as well. A recent advisory board comprising twelve experts in hair fibers and hair loss was formed to review hair loss in relation to heredity, aging and environmental factors emphasizing measures that may slow alopecia. The following information is a summary of their discussions on these topics.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.391

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 communication0.0000.001
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 head0.014
GPT teacher head0.325
Teacher spread0.311 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

Explore more

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