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Record W2283115560 · doi:10.1016/j.trpro.2016.02.074

Physical Internet Enabled Hyperconnected City Logistics

2016· article· en· W2283115560 on OpenAlexafffund
Teodor Gabriel Crainic, Benoît Montreuil

Bibliographic record

VenueTransportation research procedia · 2016
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsUniversité du Québec à Montréal
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCity logisticsThe InternetSustainabilityGreen logisticsBusinessHumanitarian LogisticsTraffic managementSustainable cityTransport engineeringIntegrated logistics supportEnvironmental economicsProcess managementComputer scienceEngineeringEconomicsWorld Wide Web

Abstract

fetched live from OpenAlex

City Logistics and Physical Internet are two major concepts aiming to profoundly change freight transportation and logistics for increased economic, environmental and societal efficiency and sustainability. They share several basic ideas and are complementary, City Logistics providing the final and last segments of the Physical Internet logistics and transportation networks. We present the first study of the links and synergy between them, introducing the idea of Hyperconnected City Logistics systems and its nine fundamental concepts making up a rich framework for designing efficient and sustainable urban logistics and transportation systems. We conclude with a number of research and innovation challenges.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.006
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.094
GPT teacher head0.303
Teacher spread0.209 · 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 designTheoretical or conceptual
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

Citations208
Published2016
Admission routes2
Has abstractyes

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