Bibliographic record
Abstract
Transport Canada's Transportation Development Centre, the Montreal Port Authority, and prime systems integrator DTI Telecommunications developed a system that integrates automatic equipment identification (AEI) with optical character recognition (OCR) for the automated identification of railcars and containers. In partnership with the Institut national d'optique and the Closed Circuit Television Corporation, DTI completed the integration and carried out improvements to the system's efficiency, accuracy and stability. This R&D project was undertaken to help the Port of Montreal improve the efficiency and productivity of container movement through the Port. The integration of AEI and OCR technologies into an information technology environment meets the Port community's requirement for timely and accurate information, and provides a basis for improving the level of service to customers. This report describes the final testing and integration phase associated with the delivery of an AEI/OCR system. A thorough analysis of the AEI/OCR system resulted in hardware and software modification to arrive at the optimal image transfer. The AEI/OCR system prototype achieved an overall performance improvement, with 92 percent ISO container recognition accuracy and the processing and transfer of the results in an electronic data interchange file within 20 minutes. Benefits from the real-time diffusion of container information include an increase in operation efficiency, reduction of paper records, and advance notice to accelerate the planning and decision-making processes.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".