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Record W2137872550 · doi:10.5539/ibr.v3n3p194

Development Mode of Automotive Logistics and Optimizing Countermeasure of China’s Automotive Enterprises

2010· article· en· W2137872550 on OpenAlexvenueno aff
Yebiao Liu, Jun Huang, Qi Zhang

Bibliographic record

VenueInternational Business Research · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicE-commerce and Technology Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsAutomotive industryCountermeasureChinaBusinessManufacturing engineeringIndustrial organizationEngineering

Abstract

fetched live from OpenAlex

With the rapid development of automotive industry and Logistics, automotive logistics which is the most complicated in logistics develops rapidly, but there is a wide gap in automotive logistics between China and foreign countries. In this paper, major logistics operation modes of domestic and foreign automotive industry are introduced, and many problems of Chinese automotive logistics are summarized on the basis of comparison and analysis. The optimizing countermeasure is brought forward to instruct China’s automotive logistics to develop better and more rapidly.

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)0.000
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: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.344
Teacher spread0.293 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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