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Record W1492319644 · doi:10.18757/ejtir.2002.2.1.3674

Flexibility of Freight Transport Sectors

2002· article· en· W1492319644 on OpenAlexaff
Hens Runhaar, Rob van der Heijden, Bart Kuipers

Bibliographic record

VenueEuropean journal of transport and infrastructure research · 2002
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsTransport Canada
FundersTechnische Universiteit Delft
KeywordsHaulageBusinessTransport engineeringTransshipment (information security)Industrial organizationInvestment (military)Delphi methodProfit (economics)Operations researchEconomicsComputer scienceEngineeringMicroeconomics

Abstract

fetched live from OpenAlex

In this paper, we explore how government policies in the field of transport may affect future freight rates, transit times, and delivery reliability. In particular, attention is paid to the ‘absorptive capacity’ of freight carriers, i.e. the extent to which they can reduce the effects of such policies by adapting their operations. Two policy scenarios are examined: one in which marginal social cost pricing becomes policy in the European Union, and one in which investment in infrastructure networks is insufficient to accommodate the future increase in traffic demand, inducing a strong increase in congestion. Six modes of transportation are included in the analysis, namely road haulage, rail transport, inland navigation, short sea shipping, airfreight, and deep sea container shipping. The study relied on experts’ opinions and estimations, which were collected by means of a Delphi survey. The expectation is that notably road transport will face difficulties in coping with the two scenarios. Its absorptive capacity proves to be the lowest, which deteriorates its competitive position vis-à-vis other modes. Yet, a weakened competitive position of road haulage is also expected autonomously.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.045
GPT teacher head0.257
Teacher spread0.212 · 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
Published2002
Admission routes1
Has abstractyes

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