Spill Response Evaluation Using an Oil Spill Model
Bibliographic record
Abstract
Numerical simulation was used to evaluate the effectiveness of an oil spill response plan developed by Western Canada Marine Response Corporation (WCMRC) for the southwest coast of Canada. The plan was part of the permitting process for a proposed terminal expansion that would result in an increase in tanker traffic. The purpose of this response evaluation was to point the way to the development of a risk-informed enhanced oil spill response capacity that would be capable of managing large spills in coastal British Columbia. The oil spill weathering and tracking model, SPILLCALC, was used for the evaluation, and was modified to meet the needs of this study. Three-dimensional currents and water properties were provided by the hydrodynamic model, H3D; waves were simulated using the wave model, SWAN, and winds were obtained from the local network of coastal light stations and wind buoys. Booms and skimmers were the two primarily mitigation methods considered here. Mitigation inputs such as deployment time, storage capacity and speed were based on existing and proposed equipment stored in the main WCMRC facility and at outlying caches. Results confirmed the need to reduce the time to first response due to the effects of currents on the floating oil and the close proximity of shorelines along the proposed shipping route. In addition, results validated the need to upgrade availability of early on-water storage capacity, which could be met by a large fast storage vessel, enabling the spill response to be more efficient and to obtain a much higher recovery rate.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".