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Record W2249319055

Developing Freight Fluidity Performance Measures: Supply Chain Perspective onFreight System Performance. Summary of a Workshop, May 21-22, 2014, Washington, D.C.

2014· article· en· W2249319055 on OpenAlexaboutno aff
K F Turnbull

Bibliographic record

VenueTransportation research circular · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransportation Systems and Infrastructure
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)Supply chainBreakoutAgency (philosophy)Variety (cybernetics)Supply chain managementBusinessTransport engineeringPrivate sectorPerformance measurementEngineeringMarketingOperations managementFinanceComputer scienceEconomics
DOInot available

Abstract

fetched live from OpenAlex

The freight transportation system is key to the global competitiveness of the United States. While the performance of the freight system is invisible to most Americans, it is a major concern to businesses, manufacturers, shippers, carriers, and network managers. The multimodal freight transportation system is managed and operated by a variety of public and private entities that monitor and measure system performance in different ways. The Transportation Research Board, in collaboration with the Federal Highway Administration Office of Freight Management and Operations, hosted a workshop to examine freight fluidity as a measure of overall supply chain performance and to explore its use in managing and improving the performance of the freight system. The workshop was held May 21–22, 2014, in Washington, D.C. The workshop brought together public agency personnel and private-sector supply chain managers to share information on monitoring and measuring different elements of the freight transportation system. The opportunities and challenges involved in expanding the use of the freight fluidity concept were discussed by participants. The workshop included general sessions and breakout sessions. The first general session focused on private-sector perspectives on measuring supply chain performance. The Canadian experience with developing and using freight system fluidity measures was featured in the second general session. Speakers in the third session presented examples of applying freight fluidity in the United States. Breakout sessions provided participants with the opportunity to discuss stakeholders and users, scalability, performance measures, data characteristics, and research needs to help advance the development and use of freight fluidity. This document presents the proceedings from the workshop. The major topics addressed by speakers in the general sessions and the discussions in breakout sessions are summarized.

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)0.010
metaresearch head score (Gemma)0.005
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: Other · Consensus signal: Other
Teacher disagreement score0.017
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0070.007
Open science0.0020.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0170.004

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 head0.036
GPT teacher head0.268
Teacher spread0.231 · 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
GenreOther

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

Citations0
Published2014
Admission routes1
Has abstractyes

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