Developing an Empirical Pipeline and Rail Crude Oil Mode Split and Route Assignment Model
Bibliographic record
Abstract
In recent years, improvements in pipeline capacities and connectivity have been inhibited by environmental and political concerns (e.g., the Dakota and Keystone XL pipeline expansion projects). This has resulted in a greater dependency on rail transport, and this modal shift of crude oil from pipeline to rail is likely to become more pronounced in the future. Therefore, there is a need to assess the impacts of future changes in pipeline/rail network connectivity, modal attributes, and shipment protocols, on the expected pattern of crude oil shipments. This paper presents a rule-based mode split and route assignment model that reflects real-world allocation, assignment, and apportionment rules. The decision-making process underlying this model is the shipper, who is prioritized by the carrier when there is limited pipeline capacity available. The proposed algorithm allows for the inclusion of crude oil shipments and the pipeline network into conventional freight demand models by capturing the complex interactions of crude shippers, pipeline carriers, and regulatory bodies. This paper demonstrates how the model can be used to predict changes in Canadian crude oil flow patterns and mode shares subject to changes in specific transportation network attributes or crude oil demands.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".