An Update on Techniques and Technologies which are Available for Responders in Response to Potential Spills of Emulsified Fuel (Orimulsion®)
Bibliographic record
Abstract
ABSTRACT Since the introduction of the emulsified fuel, ORIMULSION®, to the power generation market in the early nineties, significant work has been undertaken studying the behaviour of response to, and potential cleanup techniques for a possible spill of this fuel. The studies have in particular been carried out by sponsorships funded by BITOR, the producer of ORIMULSION®, but an important part has also been sponsored by US and Canadian government agencies. One of the most recent contributions is the report, “Spills of Emulsified Fuels, Risks and Response” a project under the US National Research Council (NRC). To date there have been no significant spills of ORIMULSION® to enable full scale use, under real life cleanup conditions, of the various conventional and new techniques and technologies, which have been developed and tested for the purpose. However, several developments and tests over the past decade and especially over the past three years, sponsored by BITOR, the Canadian Coast Guard (CCG), and Environment Canada (EC), combined with a sound portion of experience with the response to spills of heavy oil, make it most relevant to present a hands-on review of the available response options. Since ORIMULSION® has a surfactant added in the form of its emulsifying surfactant, a spill at open sea may be considered a spill of a pre-dispersed oil. Consequently, the preferred offshore response methodology would be to monitor the naturally dispersed bitumen plume. Therefore, this paper will mainly cover monitoring and response to near-shore and dockside spills in salt-, brackish-, and fresh water, which might call for a clean-up effort, as well as shoreline protection and clean-up.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".