Modelling long-term multi-species impacts from oil spill scenarios using Salish Sea Atlantis
Bibliographic record
Abstract
The Salish Sea is a major transportation route for oil between Canada and the USA. Continued oil transportation leads to concerns about the impact of spills on the surrounding ecosystem. Short-term impacts of oiling are more apparent and heavily studied, but the long-term effects of oil contamination throughout the ecosystem are not well known. A Salish Sea Atlantis whole-ecosystem model has been developed that allows us to explore the effects of contaminants on future trophic interactions. We used Atlantis, forced with time-series fields of water currents, temperature and salinity from the SalishSeaCast NEMO Model, to examine the impact of oil spills on key flora and fauna within the region. In collaboration with the Commonwealth Scientific and Industrial Research Organisation (CSIRO), here we present the results of specific oil spill scenarios developed in conjunction with the Department of Fisheries & Oceans Canada (DFO-Pacific) and Transport Canada. We focus on the potential long-term impacts of point source hydrocarbon contamination throughout the food chain. This includes groups of primary producers such as phytoplankton and seagrass, important fish species including salmon, as well as large 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".