Bibliographic record
Abstract
Deltaport is the largest container terminal in Canada. It is located in the Port of Vancouver and operated by Terminal Systems Inc (TSI). Approximately 60% of the vessel throughput at Deltaport moves via the on-terminal intermodal railyard (IY). The Port, TSI, and BC Rail (the rail operator for switching) felt that the IY, in its current configuration, was the limiting factor in overall terminal capacity and undertook a detailed simulation study to determine how to accommodate a growth of approximately 50% through the IY. The IY is served by rail-mounted gantry cranes (RMGs). The study first analyzed historical IY RMG productivity. Historical data was used to calibrate simulation models of terminal activity which include rail, vessel, and gate moves. Once a successful calibration was achieved, future cases corresponding to annual throughput of 2.4M annual vessel TEU were simulated with various combinations of numbers of tractors, yard cranes and rail switching. Output from these models of future peak shift operations was used to define a range of likely RMG productivity levels for future operations. This data, along with peaking factors and estimates of downtime for train switching, along with the minimum practical operating distance between RMGs were used to develop annual lift capacity for the IY. These capacity figures were compared to previously calculated values for berth and container yard operations to ensure that the facility was capable of handling 2.4M TEU overall. At the start of the study, it was assumed that the IY would need to be extended in length in order to accommodate the target throughput. The study indicated that - by adding working tracks in parallel, adding RMGs and identifying rail switching methods - TSI should be able to meet the target volume within the existing IY footprint.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".