Using Open Source Data to Quantify the Impact of Supply Chain Disruptions at Niche Ports: Scenario Involving Canada’s Largest Oil Refinery
Bibliographic record
Abstract
Niche ports typically are smaller marine ports that specialize in handling specific commodities and can be the interface between import and export of bulk commodities from landside facilities, such as oil refineries. Specialization can make port-dependent industries and their customers particularly vulnerable to disruptions of port operations because the specialized handling facilities may be unique to the port. Industries may not be able to shift their supply to other modes, such as rail. This paper outlines the use of automatic identification system transponder data from oceangoing vessels, in concert with open source data, to quantify first-order impacts of a supply chain disruption at a niche port. Results are presented from a study of a Canadian niche port where a worst-reasonable-case scenario of a 1-week disruption to oil imports and exports would delay delivery of an estimated 246 million L of fuel to New England for at least a week; Boston, Massachusetts, and Portland, Maine, would be most affected. The approach used in this study could be a valuable screening tool for public safety agencies wanting a better understanding of vulnerabilities and interdependencies at niche ports in situations in which detailed simulations and modeling are impractical.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".