DEVELOPING AND TESTING A MULTIMODAL ELECTRONIC SUPPLY CHAIN MANIFEST
Bibliographic record
Abstract
The global economy is being transformed. Increasing global competition is being witnessed as more nations embrace technology and place an increased emphasis on external trade. Increasing pressure is being applied to all business sectors to maintain ambitious production schedules, improve customer service and communications, and expedite the flow of goods to market and end-users, while at the same trying to minimize costs in all aspects. Time pressures to deliver cargo more quickly than ever are focusing attention on ground-air intermodalism. Truck-to-air cargo movements are rising at a brisk pace. The concern is exacerbated by the fact that much of the air cargo is transported on passenger planes. The events of last September opened the eyes of the world to the vulnerability in traditional air security measures. In responding to these factors, the federal government has identified air cargo security as a primary area of vulnerability. A crucial aspect impacting air cargo security concerns controlling access to cargo and information flows at all points in the distribution chain. Fortunately through technological advancements, applications now exist that can dramatically improve the electronic flow of information to all points in the distribution chain enabling process efficiencies and new levels of cargo scrutiny and security. The government seized on the opportunity to partner with the private sector to develop an technological answer to improve efficiency and security in air freight transport. Several federal and state agencies joined forces with the American Transportation Research Institute (formerly the ATA Foundation) and its private sector partners to develop and operationally test an Electronic Multimodal Distribution Chain Manifest system. The Phase II Electronic Supply Chain Manifest (ESCM) operational field test developed and tested a secure electronic manifest system. The field operational test was conducted in conjunction with manufacturing, trucking, and airline participants in the Chicago-O'Hare International Airport and New York City-JFK International Airport service areas. The Phase II project used encrypted Internet with biometric smartcard technologies to improve intermodal communications between distribution chain partners and enhance cargo safety and security. It also increased productivity by speeding up cargo processing, reducing manifest lead times, and reducing the probability of human error during information entry while simultaneously drastically improving the ability to prevent dangerous cargo from progressing downstream in the distribution chain. A Phase III project is currently extending the ESCM testing program by adding Los Angeles International Airport and Toronto's Pearson International Airport to the fold.
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 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".