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Record W2003793882 · doi:10.1287/trsc.1090.0276

<b>Guest Editorial</b>—Focused Issue on Freight Transportation

2009· article· en· W2003793882 on OpenAlexaff
Teodor Gabriel Crainic, Vedat Verter

Bibliographic record

VenueTransportation Science · 2009
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsMcGill UniversityHEC MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsTraffic managementTransport engineeringBusinessTransportation planningHumanitarian LogisticsTransportation industryEngineeringIndustrial organization

Abstract

fetched live from OpenAlex

The transportation of freight embodies exchanges and commerce in human society and, thus, constitutes a major enabling factor for most of our economic and social activities. The importance of this role is continuously increasing, as do the requirements for freight transportation, to be efficient, reliable, flexible, and responsive to our continuously evolving and, sometimes, contradictory needs and expectations. These requirements challenge operations research and transportation science to continuously enhance our methodological capabilities and innovatively apply them to important issues in the analysis, planning, and management of freight transportation and logistics.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.031
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0060.004
Open science0.0030.001
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0310.024

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 head0.013
GPT teacher head0.272
Teacher spread0.259 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations1
Published2009
Admission routes1
Has abstractyes

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