An Analysis of Selected Oil Spill Case Studies on the Shorelines of Canada
Bibliographic record
Abstract
After an oil spill, oil may wash ashore and there is only a short window of opportunity to respond. Analysis of historical incident data is valuable to guide future responses and cleanup practices. This study summarized the oil spill accidents that impacted the Canadian shoreline and analyzed the related information including location, incident characteristics, and shoreline treatment. Major spills due to tanker accidents in Canadian marine waters fortunately have been infrequent. Most of the accidents have happened on Canada’s Pacific coast, accounting for 52% of the total accidents recorded. The Atlantic coast accounted for 39% and the remaining accidents happened in the Arctic region. Regarding spilled volume, 55% of the accidents spilled oil volumes smaller than 100 m3. Spilled volumes between 100 ~ 1000 m3 represent 30% of the incidents and 15% had spilled volume greater than 1000 m3. Bunker C fuel and diesel were the main types of the spilled oil, accounting for 33% of the spills, respectively. Within the oil spill accidents impacting Canadian shore- lines, marine vessel accidents were the major sources accounting for 70% of the spill accidents. In terms of the shoreline treatment, the commonly employed treatments were manual, vacuum, mechanical, and sorbent removal. The dataset highlighted the significance of a more comprehensive record for response phase details and environmental effects monitoring.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".