VALIDATION OF AN OPERATIONAL AEI/OCR SYSTEM
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.
Bibliographic record
Abstract
There is a growing need in the intermodal industry for better tracking of containers in transit in order to improve handling and throughput, increase security, and enable the use of electronic data interchange (EDI). Since radio frequency (RF) tags or electronic seals (e-seals) are not standardized in the container shipping industry, automation of the container recognition process must be achieved by using the identification numbers printed on the containers. It is in this context that the Transportation Development Center (TDC) of Transport Canada, the Montreal Port Authority, and the prime systems integrator DTI Telecommunications have developed and delivered a system that integrates automatic equipment identification (AEI) with an optical character recognition (OCR) system to automate the identification of railcars and containers. This paper describes the final testing and integration phase associated with the delivery of an AEI system with a proprietary, state-of-the-art OCR system for automatic identification of railcars and containers. The integration of these two systems into an information technology (IT) environment meets the Port community's requirement for timely, accurate information, and provides a basis for customer service improvement.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it