A Container Terminal at the Port of Prince Rupert: considerations from a transportation perspective
Bibliographic record
Abstract
This paper discusses the transport of containers between the Port of Prince Rupert and the hinterland. The result of several data collection and analysis efforts, we present a set of findings regarding the role Prince Rupert might play in North American transportation, and the particular strengths or weaknesses of this facility. In the short term, there will be no facilities for container rehandling in Prince Rupert. This activity may take place in Prince George, or more likely these importers may continue to use the facilities surrounding Vancouver. Given the current rail network, Prince George will be required to handle as much cargo as Prince Rupert. This will have negative impacts on air quality in Prince George, yet potentially positive impacts on employment and economic activity through rehandling and transloading opportunities. Due to sizeable resources in Western Canada, this Port may offer a better ratio of exports to imports than other West Coast terminals which will attract steamship lines looking for west-bound fares. Unfortunately, on multi-stop routes, this may also mean Prince Rupert is visited after other West Coast destinations, thereby reducing the benefit of being closer to Asia. Despite these uncertainties, we conclude that, because of its unique features, this Port has strong potential to impact logistics practices in a continental transportation system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".