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Record W2597839649

Links between production system and transport. The example of German and French industries

2010· article· en· W2597839649 on OpenAlexaff
M Guilbault, Élisabeth Gouvernal, Marie Lebaudy, Barbara Lenz, Sebastian P. Schneider, Katja Köhler

Bibliographic record

Venueelib (German Aerospace Center) · 2010
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsMinistère des Transports
Fundersnot available
KeywordsProduction (economics)Context (archaeology)Distribution (mathematics)BusinessGermanEconomicsIndustrial organizationEconomyGeography
DOInot available

Abstract

fetched live from OpenAlex

Changes in transportation are closely linked to the economic and logistical characteristics of the production system. The aim of this paper is, on the basis of data on the economic context and surveys conducted in France (the 1988 Shipper survey and the 2004 ECHO survey) and in Germany (the 2005 DLR survey), to show the major changes that have occurred in the two countries at both micro- and macro-economic levels and how these have affected transport demand.
\nThe first level of analysis relates to changes in the economic fabric. In particular, we have demonstrated the growing proportion of small and medium-sized firms at a time when large production units, which are those that are best able to concentrate their freight and use modes other than the road, are becoming fewer and fewer. At the same time, economic links are becoming more complex and, in the case of France, we have shown the increasing role played by wholesale traders in the distribution of goods. The constant reduction in transport costs and the opening up of markets is another structural factor whose impacts in both France and Germany we have also analysed. In addition to these economic changes, the internal modes of production of firms have also changed. Production is becoming more diversified and Just-in-time practices are spreading. The fragmentation of freight flows is thus occurring both in space and in time, which also has a major effect on the characteristics of the flows generated by firms and changes in transport. In the case of all these changes we have attempted to show the differences and similarities between the ways transport has changed in the two countries.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.476

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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 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
Published2010
Admission routes1
Has abstractyes

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