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Record W3004346872 · doi:10.5539/ass.v16n2p51

Yang Yuelin and Modern Sizing Technology in China

2020· article· en· W3004346872 on OpenAlexvenueno aff
Wen Zhang, Xiaoming Yang

Bibliographic record

VenueAsian Social Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsnot available
FundersFundamental Research Funds for the Central UniversitiesDonghua University
KeywordsEnthusiasmChinaSizingIndustrialisationPromotion (chess)TextileWork (physics)EngineeringLawPolitical scienceHistoryPsychologyMechanical engineeringArtVisual arts

Abstract

fetched live from OpenAlex

Since modern times, China has been forced into the huge wave of world industrialization. Yang Yuelin, a famous textile engineering expert and educator, is one of the countless people with lofty ideals who want to serve the country and embark on the road of "saving the country through industry". Yang Yuelin wrote a book "theoretical and practical sizing theory" after he learned the sizing technology of China and the West. The sizing principles summarized in the book constitute the theoretical basis of "sizing working method" of Qingdao Textile administration, which has a great driving role for sizing work in China. In the early days of the founding of the people's Republic of China, Yang Yuelin also actively responded to the call of the state and actively participated in the summary and promotion of the "Hao Jianxiu spinning method", which greatly encouraged the enthusiasm of the textile workers and their enthusiasm for production.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.289
Teacher spread0.267 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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