Applying Supply Chain Logistics Modeling to Border Security and Efficiency
Bibliographic record
Abstract
This paper describes the process mapping of cross-border trucking from Canada into the United States and demonstrates how simulation modeling based on this can be used to identify and quantify strategic and operational choices in both the public and private sector. Increasing security concerns have led to the increase of cross-border regulations, heightened awareness of international transportation and the implementation of systems such as the Intelligent Transportation Systems (ITS). Security, especially along the length of the entire area of the supply chain, has become the most important concern. Said security changes will be reviewed in congruence with service quality for users as well as the expected increase in cost for both the transportation industry and public infrastructure providers. Public and private sectors must make decisions on these changes. The necessary Benefit-Cost and Return on Investment calculations are dependent upon on the accurate estimation of the impact of security, productivity, service and cost. The use of process mapping and simulation models, which can efficiently estimate process change impacts under different regulatory and informational technology scenarios, can benefit the cost estimation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".