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Record W1981399398 · doi:10.1016/j.trpro.2014.10.041

Performance Indicators for Planning Intermodal Barge Transportation Systems

2014· article· en· W1981399398 on OpenAlex
Yunfei Wang, Ioana Bilegan, Teodor Gabriel Crainic, Abdelhakim Artiba

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTransportation research procedia · 2014
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsUniversité LavalUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBARGEPerformance indicatorRevenueTransportation planningTransport engineeringPublic transportPerformance measurementOperations researchComputer scienceRisk analysis (engineering)BusinessEngineeringMarketing

Abstract

fetched live from OpenAlex

Various indicators are used to qualify the performance of intermodal transportation systems. Some of these are found in public documents, usually providing global measures such as total flow volumes, profits, and share values. While of great interest, such measures are not sufficient to support a fine analysis of different operation strategies, commercial policies, and planning methods. Additional measures are used in the scientific literature to address these issues. Our first goal is to review the performance indicators found in scientific literature and to qualify them with respect to tactical planning of intermodal barge transportation systems. We extend this analysis to include revenue management policies, a topic generally neglected in freight transportation. We also discuss procedures to generate problem instances that provide the means to analyze planning methods and system behavior based on these performance indicators.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.579

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.311
Teacher spread0.269 · 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