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Diversity of logistics centre concepts in Europe

2018· article· en· W2910757023 on OpenAlexaff
Maciej Mindur, Katarzyna Turoń, Grzegorz Sierpiński

Bibliographic record

VenueLogistics and Transport · 2018
Typearticle
Languageen
FieldSocial Sciences
Topictransportation and logistics systems
Canadian institutionsTransport Canada
Fundersnot available
KeywordsVariety (cybernetics)Diversity (politics)European unionBusinessCorporate social responsibilityProcess (computing)Relation (database)Sustainable developmentRegional scienceMarketingIndustrial organizationProcess managementPolitical sciencePublic relationsSociologyInternational tradeComputer science

Abstract

fetched live from OpenAlex

The article discusses logistic centres in Europe, legal changes and directions for their development in the European Union that has led to an increased cross border flow of goods and the need to continue the process. The article identifies major features of logistics centres and presents a comparison of various types of conceptual objectives for logistics centres in Europe. The analysis has been used to develop a list of features collated with the variety of concepts and current market requirements in relation to such issues as sustainable development and corporate social responsibility.

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)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.007
Science and technology studies0.0030.011
Scholarly communication0.0150.006
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.046
GPT teacher head0.312
Teacher spread0.266 · 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 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

Citations2
Published2018
Admission routes1
Has abstractyes

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