Oil spills and marine mammals in British Columbia, Canada: development and application of a risk-based conceptual framework
Bibliographic record
Abstract
Marine mammals are inherently vulnerable to oil spills. We developed a conceptual framework to evaluate the impacts of potential oil exposure on marine mammals and applied it to 21 species inhabiting coastal British Columbia (BC), Canada. Oil spill vulnerability was determined by examining both the likelihood of species-specific (individual) oil exposure and the consequent likelihood of population-level effects. Oil exposure pathways, ecology, and physiological characteristics were first used to assign species—specific vulnerability rankings. Baleen whales were found to be highly vulnerable due to blowhole breathing, surface filter feeding, and invertebrate prey. Sea otters (Enhydra lutris) were ranked as highly vulnerable due to their time spent at the ocean surface, dense pelage, and benthic feeding techniques. Species-specific vulnerabilities were considered to estimate the likelihood of population- level effects occurring after oil exposure. Killer whale (Orcinus orca) populations were deemed at highest risk due to small population sizes, complex social structure, long lives, slow reproductive turnover, and dietary specialization. Finally, we related the species–specific and population-level vulnerabilities. In BC, vulnerability was deemed highest for Northern and Southern Resident killer whales and sea otters, followed by Bigg’s killer whales and Steller sea lions (Eumetopias jubatus). Our findings challenge the typical ‘‘indicator species’’ approach routinely used and underscore the need to examine marine mammals at a species and population level for risk-based oil spill predictions. This conceptual framework can be combined with spill probabilities and volumes to develop more robust risk assessments and may be applied elsewhere to identify vulnerability themes for marine mammals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".