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Record W2607294733 · doi:10.5430/jbar.v6n1p52

Description and Classification of the Youth’s Body Type in Shanghai

2017· article· en· W2607294733 on OpenAlexvenueno aff
Shengye Du, Ling Zhou, Mengfei Xu, Jing Bai, Youjie Zeng

Bibliographic record

VenueJournal of Business Administration Research · 2017
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsnot available
Fundersnot available
KeywordsBody shapeBody typeClothingIdeal (ethics)Cluster (spacecraft)Variance (accounting)StatisticsPsychologyComputer scienceMathematicsArtificial intelligenceGeographyMedicineEpistemology

Abstract

fetched live from OpenAlex

The carried out standard of the garment size is not able to reflect the body shapes of the modern. In order to achieve a better standard, which is in accord with the requirement of the modern, the figures of 520 women and men, all in the age of 18 to 40, are measured. 18 variables of body shapes are chosen, and 7 variables are derived from them. All the data is processed by SPSS, and one-way analysis of variance (ANOVA) and cluster analysis are used. Both of women’s upper and lower body shapes are classified into 6 categories, and the similar segregation is carried on men with 4 categories, which is an ideal classification. The results offer the support for understanding the current situation of the body shapes of young women and men in Shanghai, for customizing a new standard, and for providing theoretical basis and reference on optimization of designs of clothing structure.

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)0.003
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.952
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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)0.0000.000

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 head0.286
GPT teacher head0.428
Teacher spread0.142 · 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 designBench or experimental
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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