Planning and Responding to a Petrochemical Disaster in the North American Inland Seas
Bibliographic record
Abstract
The Great Lakes represent the largest group of freshwater lakes in the world along a 1,500 mile international boundary between the United States and Canada. A source of drinking water for 35 million people and a hub of unique biodiversity, a major petrochemical spill would be devastating. With the increase in pipeline activity due to regional tar sands drilling and the navigationally challenging waterways hosting an increasing stream of petrochemical commerce, risk to the Lakes is higher than ever. Given the Lake's closed-system nature and their geographic remoteness relative to current US and Canadian government and private sector assets, the current response posture is inadequate. As the primary maritime spill response agency in the United States, the US Coast Guard retains the mantle of prevention and planning for a Great Lakes petrochemical disaster. This paper seeks to examine the historic, current, and future states of the Great Lakes' oil-spill risk, in light of increased maritime commerce and recent spill “near-misses” regarding submerged pipelines in the Straits of Mackinac. The US Congress and the US Coast Guard have identified that the Great Lakes are not prepared for a large scale spill. Current resourcing levels and technology are insufficient, especially given the challenge of responding while the Lakes are frozen for a substantial portion of the year. With resources focused on the prospect of disaster in salt water regions, the “inland seas” of the Great Lakes receive too little attention. After identifying the evolution of Great Lakes spill prevention and response policy, this paper will apply the Gulf of Mexico Deepwater Horizon spill as a case study into what spill response would look like on the Great Lakes. Given the authors' expansive experience as an operator during that historic spill and current commander of one of two US oil spill response assets in the Great Lakes, this paper will also identify remaining challenges to an effective spill response policy, and conclude with recommendations on how to tackle the response issues identified. The US Congress recently established the US Coast Guard's National Center of Expertise for the Great Lakes and one of their primary tasks is to analyze the effect of a spill in freshwater and develop an appropriate response plan. By attempting to identify critical gaps, this paper seeks to advance government and industry's ability to posture the region swiftly in the face of a growing threat and assist in the Center's work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".