DISPERSANT EFFECTIVENESS AS A FUNCTION OF ENERGY DISSIPATION RATE IN AN EXPERIMENTAL WAVE TANK
Bibliographic record
Abstract
ABSTRACT In 2005, the National Research Council (NRC) published a comprehensive treatise on oil spill dispersants. Among other things, it concluded that research on dispersion effectiveness as a function of energy dissipation rate and particle size distribution was a high priority. Energy dissipation rate (turbulence and existence of breaking waves) is important to initiate and promote effective dispersion, and the particle size distribution of dispersed oil droplets affects dispersion and the ultimate fate of oil in the water column. In this paper, we discuss the use of a wave tank built on the premises of the Bedford Institute of Oceanography, Dartmouth, Nova Scotia, Canada as part of collaborative research begun in 2003 by the U.S. Environmental Protection Agency (EPA) and Fisheries and Oceans Canada (DFO). This tank is able to produce breaking waves of various energy levels at precise locations in the tank. We studied the effects of 2 commercial dispersants (Corexit 9500 and SPC 1000) and a no dispersant control on two different crude oils (unweathered Alaska North Slope and weathered MESA Light) at 3 different energy dissipation rates (regular non-breaking waves, spilling breakers, and plunging breakers), amounting to 18 different treatments. We quantified the energy dissipation rates under those 3 wave conditions and measured oil dispersion in a factorial experiment involving 3 replicates of the 18 treatments over the course of the summer of 2006. Results clearly showed the importance of wave energy and the presence of a chemical dispersant on the ability to produce effective dispersion of oil into the water column. The presence of dispersants at increasing wave energies produced significantly better dispersion (p <0.05) than the no-dispersant controls. This study was conducted under batch conditions. Future work will be done under continuous flow conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".