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Record W2781060793 · doi:10.5539/ass.v14n1p136

The Feasibility and Methodology for Water and Land Paintings in the Study of the Ming Dynasty Costumes

2017· article· en· W2781060793 on OpenAlexvenueno aff
Xiangyang Bian, Meng Niu

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsnot available
FundersUniversity of California, DavisDonghua University
KeywordsPaintingChinese paintingArtTexture (cosmology)Visual artsComputer scienceImage (mathematics)Artificial intelligence

Abstract

fetched live from OpenAlex

Chinese Water and Land painting contains lots of figure costume modeling, providing intuitive and vivid image data for the study of ancient costumes. It is a new medium for the study of Chinese ancient costumes. This paper analyzes the feasibility and methodology for Water and Land paintings in the study of the Ming Dynasty. With the Ming costumes in the Water and Land Paintings, this paper discusses the shape, color, texture and fabric patterns of the ancient dresses. The feasibility of using Water and Land Paintings to study ancient costumes is further analyzed in this paper. This paper emphasizes the importance of ancient dresses in Water and Land Painting for the study of its wearing effect, and the specific methods of research that are put forward.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.193
GPT teacher head0.370
Teacher spread0.177 · 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 designQualitative
Domainnot available
GenreMethods

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
Published2017
Admission routes1
Has abstractyes

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