Bibliographic record
Abstract
Canada Customs is currently faced with problems of overcrowding. This situation causes lost revenue and potential control problems, and detracts from its public image. Customs administration is interested in determining what changes, if any, can be made to help alleviate the problem. The goal of this paper is to analyze several alternatives which are available to customs officials, and to recommend changes which can be implemented to improve the current situation. To achieve this goal, several applications of Management Science methods were performed. The first application involved a cost-benefit analysis of the Peace Arch Crossing Entry (PACE) Project. It was found that the PACE project has positive net benefits of approximately $9.5 million provided that it is supported by sufficient staff levels. Results of the cost-benefit analysis also indicate that the social benefits of having an extra staff member available far outweigh the costs involved. In the second application performed, multiple regression techniques were used to analyze various characteristics of travellers which affect service time within the customs office. As a result, it is recommended that customs officials look into the possibility of keeping certain groups of individuals (whose processing times are relatively low) separate from the main flow of travellers, in order to save these travellers a great deal of time. In the third application, simulation and animation were used to replicate the current situation in the Pacific Highway Customs Office. Experiments were then conducted to determine the optimal mix of regular terminals and Self Declaration System (SDS) terminals. It was found that implementing SDS was not as efficient as adding a new regular terminal. It is recommended that a study be conducted to determine by how much the times for SDS can be reduced. If significant reductions in time are not possible, it may be in the best interest of all concerned to revert the SDS terminals to regular terminals.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.458 | 0.210 |
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".