Reducing the Environmental Impacts of Remote Ports: The Example of Prince Rupert
Bibliographic record
Abstract
The west coast ports of North America are prime examples of twentieth-century ports that must adapt to twenty-first century concerns. Located in urban areas, the ports of Vancouver, British Columbia, Seattle and Tacoma, Washington, and California are increasingly viewed as undesirable neighbors by urban residents. Because these large population centers have diversified economic bases, the economic contributions of ports are not as visible, or as crucial, to residents as in the past. This reduces their tolerance for port impacts. Constraints on developable land and congestion add to the challenges faced by these ports. As trade and container traffic increase, these ports must increase in size, in throughput, or both in order to compete for global trade. As discussed further below, however, this growth must take place without imposing additional externalities on neighbors who are increasingly aware of the burden that ports place on those who live around them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".