An Analysis of Selected Oil Spill Case Studies on the Shorelines of Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it