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Record W4297930773

Transport and Logistics Demand: New Input from Large Surveys of Shipper in France

2010· preprint· en· W4297930773 on OpenAlexaff
M Guilbault, E Gouvernal

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2010
Typepreprint
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsMinistère des Transports
Fundersnot available
KeywordsBusinessTransport engineeringOperations researchIndustrial organizationOperations managementEngineering
DOInot available

Abstract

fetched live from OpenAlex

Freight transport is closely linked to industrial and commercial activities and can be seen as a fully integrated element of the production process. Shipper production and logistic constraints play an important role in the choice of transport solutions. However, there is little statistical data on which to base an analysis of the relationship between transport and its production and logistics related determinants. For this reason two large national surveys were undertaken in France to provide us with new empirical data. The paper presents the methodological aspects of these surveys (the 1988 Shipper Survey and the 2004 ECHO Survey). The use of 'the shipment' as the measurement unit instead of the usual tonne or tonne-kilometre approach is one of the major innovations of these surveys which provide a new statistical insight. Another particularity is the tracking of the shipment and the identification of each transport leg and operator involved in the shipment. The whole transport chain is described, from both the physical and organizational standpoint. A selection of results, indicators and trends, is presented with a focus on the main changes that have occurred in recent years based on a comparison between the two datasets. Special attention is paid to the relationship between transport choices and the production characteristics of sites and the increasing spatial and temporal fragmentation of shipments which makes the choice of modes other than the road increasingly difficult.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 science0.0010.000
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 head0.012
GPT teacher head0.208
Teacher spread0.196 · 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
Published2010
Admission routes1
Has abstractyes

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