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Record W1930672895

Urban logistics solutions and financing mechanisms : a scenario assessment analysis

2013· article· en· W1930672895 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOpenstarTs (Univeristy of Trieste https://www.units.it/) · 2013
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsMinistère des Transports
Fundersnot available
KeywordsAttractivenessBusinessSet (abstract data type)City logisticsCost–benefit analysisField (mathematics)Transport engineeringFinanceEnvironmental economicsRisk analysis (engineering)Computer scienceEconomicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

T
\nhis paper presents the main issues related
\nto the
\nfinancing
\nof urban logistics solutions
\n, m
\nore precise
\nly
\nto
\nthe contribution of economic analysis
\non strategic decision support related for urban logistics
\nfinancing
\n, focusing on cost benefit analysis. First
\nwe present the main
\nfunding strategies in
\nurban
\neconomics, mainly in
\nthe field of urban logistics
\n.
\nSecond we address
\nthe contribution of cost benefit
\nanalysis by recalling the main methodology and
\nadapting
\nit to urban logistics.
\nThird we apply the method
\nto the
\nexample of deploying a delivery spac
\ne booking network,
\nand illustrate the application via a set of
\nthree examples containing different situations and scenarios, which
\nare presented
\n, assessed and discussed
\n.
\nFrom the different simulations, it is observed that the way the system is financed has
\nstrong impacts on
\nboth its individual cost
\n(for potential users) and its attractiveness.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.212
Teacher spread0.171 · 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