Developing Freight Fluidity Performance Measures: Supply Chain Perspective onFreight System Performance. Summary of a Workshop, May 21-22, 2014, Washington, D.C.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".