Preparedness for Heavy Oil Spills: More Focus on Mechanical Feeder Skimmers
Bibliographic record
Abstract
ABSTRACT Numerous spills over the years have demonstrated that mechanical response to heavy oil spills at sea is less than successful. Recently, after the 10,000 m3 Erika spill in France, less than 5% was recovered before the oil reached the coast. The weather played an important role, but the selection of equipment and apparent response strategy did as well. Mainly weir skimmers were used. Weir skimmers are, due to their simplicity and reliability, widely used in spills at sea. Probably because some of the well-known brands are equipped with heavy oil transfer pumps, they commonly are used in heavy oil spills. In addition, high capacity units with light oil centrifugal transfer pumps are being used on heavy oil (Erika). Weir skimmers start losing their efficiency when the viscosity of the oil exceeds certain limits. It gets too difficult for the oil to pass the weir lip and flow into the hopper so that the pump can transfer it. For very heavy oils, which barely can float, the inlet weir is an even larger obstruction. A mechanical feeder skimmer lifts or drags the oil out of the water to a position above the water surface and feeds or drops it into a collection tank or a transfer pump. The mechanical feeder principle may result in significantly increased performance regarding high viscosity, debris, and, in most cases, low water content. Recent tests at SAIC/Environment Canada's test facility in Ottawa, Canada, sponsored by the Canadian Coast Guard, demonstrated that floating bitumen with a viscosity of about 2 million cSt could be recovered by mechanical feeder skimmers, while a “high viscosity” weir skimmer had no effect. This puts the Erika's viscosity of “only” 200,000 cSt into perspective, and strongly point at mechanical feeder skimmers in the preparedness for heavy oil spills.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".