Modelling risk of chronic oil pollution from vessel operations in Canada's West Coast
Bibliographic record
Abstract
Chronic oil pollution or frequent small-scale oil discharges from vessel operations is an important source of marine oil pollution and considered a constant threat to marine and coastal environments. In Canada’s Pacific region, evidence of such illegal discharges has been gathered by the National Aerial Surveillance Program (NASP) from 1998 to 2007. We used this information to fit Generalized Linear Models (GLMs) for offshore waters and inshore waters and explore the relationships between oil spill occurrences and four predictor variables: shipping traffic densities for different vessel types, distance to shore, distance to port and density of small harbours. The best-fit models for both regions show that areas closer to shore and with higher density of small harbours have a higher probability of oil spill occurrences. However, only in inshore waters was shipping traffic significantly related with oil spill occurrences. Tug boats and oil tanker traffic show a significant positive relationship with oil spill observations, while carriers presented a negative association. Mapped results for offshore areas depicted the highest probability of oily discharges in Barkley Sound and at the entrance of the Juan de Fuca Strait; whereas in inshore waters, oil pollution hot spots were found in the vicinity of major commercial and tourist centres. These probability maps were used to identify Coastal and Marine Protected Areas (CMPAs) and Important Bird Areas (IBAs) more likely to be exposed to chronic oil pollution during a period of 10 years. Three areas were highlighted as the most vulnerable based on their likelihood of exposure and the sensitivity of the species they contain to oil pollution. These sites are the Tofino Mudflats, Barkley Sound, Scott Islands and the Sturgeon and Robert Banks, in the Fraser River delta. Our findings provide better understanding of the relationships between oil spill occurrences and vessel operations and help us identify likely oil pollution hot spots and sites particularly vulnerable to this stressor in Canada’s Pacific region. This information can be useful to NASP in improving its efficiency and in targeting monitoring efforts to troublesome areas. Additionally, this research contributes to regional studies that focus on analyzing the distribution of anthropogenic stressors from sea-based activities in British Columbia. Finally, we highlight the importance of collecting accurate data to properly model the probability of oil spill occurrences and encourage future research aiming to better understand and ultimately reduce the chronic release of pollutants from shipping activities into the marine environment.
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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 | 0.002 |
| 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 | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".