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Record W2738622494 · doi:10.5539/ass.v13n8p176

On a Silk Military Colour of Russia during Peter I

2017· article· en· W2738622494 on OpenAlexvenueno aff
Miao Su, Feng Zhao, Rulin Yang

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilkworms and Sericulture Research
Canadian institutionsnot available
FundersMinistry of Education of the People's Republic of China
KeywordsReignChinaTheme (computing)Style (visual arts)SILKVariety (cybernetics)AutonomyHistoryVisual artsArtLawPolitical scienceEngineeringArchaeologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

This paper studies a silk military colour during the reign of Tsar Peter I in the collection of the Swedish Army Museum, discussing the colour from the perspectives of several aspects including the background, the shape and form, the decorative theme, the fabric variety and the silk pattern. Analysis and researches on the fabric weave and pattern are the main focuses of this paper and the recovery of the fabric’s pattern is also included. And on the basis of physical research, combining with the comprehensive analysis of the wars and the style of the colour, the authors identify that these silk fabrics used for the Russian military colour are Chinese satin damask of late Ming and early Qing dynasties. d intrinsic rewards were strongly related to OC. Especially, intrinsic rewards had the strongest association with OC. These findings suggest that the antecedents of OC in Vietnam are different from those in the West and China. The comparison between university graduates and others showed that fatigue and autonomy had stronger influence on OC in university graduates than in others. Discussions and implications concerning human resource management in Vietnam are offered.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.281
Teacher spread0.260 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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