Developing and Applying Level-of-Service Framework to Land-Based Port-of-Entry Infrastructure Planning
Bibliographic record
Abstract
Major land-based ports of entry (POEs) are key surface transportation components within the global supply chain. Appropriate planning methodologies are critical for assessing whether port infrastructure is adequate to meet projected demands and support economic and trade objectives. However, the development and the application of planning methodologies to assess the delay and congestion impacts of inaction or specific port improvement scenarios have not kept pace with the growing significance of these key surface transportation assets. In response to these methodology gaps, a level-of-service (LOS) framework and analysis was developed during the Pembina–Emerson POE study (2012). The LOS framework and performance measurement algorithms for POEs were developed from LOS concepts in the Highway Capacity Manual 2010. The LOS framework and performance measurement algorithms can be applied to any major border crossing to assess port throughput with any combination of policy settings, processing times, staffing levels, or infrastructure improvement scenarios. The LOS methodology can assess port improvement scenarios and provide a standardized basis for port-to-port and border-to-border comparisons. Combining the LOS framework (a trade-off analysis) with 30th highest hour design (an infrastructure design approach) provides transportation policy makers, planners, and engineers greater flexibility to assess the implications of various port improvement scenarios, infrastructure designs, and phasing considerations as well as the potential to generate outputs that enhance economic analysis for proposed port improvement scenarios.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".