Pacific Northwest Logistics Patterns: The Port of Prince Rupert as a Successful National Gateway Strategy
Bibliographic record
Abstract
The Port of Prince Rupert is developing a significant marine container terminal (2 million TEUs by 2012). This port will be the closest major port to Southeast Alaska, and co-located with the terminus, and only Canadian port of the Alaska Marine Highway System. Located in Northern British Columbia, the Port of Prince Rupert is the second largest deep-sea port on the West Coast of Canada. This port also offers up to 58 hours shorter transit time between North America and key ports in Asia compared to other West Coast ports. The Port of Prince Rupert also presents a completely new and untested model for port development. All other major North American ports are located in major urban centers with extensive inland transportation infrastructure. This is not the case for the Port of Prince Rupert, which opened for container business in September, 2007. The port’s rural location may be viewed as both an asset and vulnerability; an asset as there is less congestion, and a smaller population for any negative exposure, while vulnerabilities arise due to possible disruptions in transporting goods to and from the hinterland on the single-track rail line which is prone to landslides and flooding and the lack of any landside handling infrastructure such as warehouses or transloading facilities. This project will gather goods movement data on trade between Alaska, Washington and British Columbia from the Bureau of Transportation Statistics and PIERS database to develop a regional flow map. Qualitative interviews with shipping lines and major transportation providers at regional ports will also be completed (over the phone). The final report will document results and analysis from these tasks and provide a framework for future research concerning the role of rural ports, in particular those in northern locations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".