MASS BALANCE AND DISPERSANT EFFECTIVENESS: ARE NEW TECHNIQUES AND METHODOLOGIES NEEDED?
Bibliographic record
Abstract
ABSTRACT In order to obtain a scientifically-acceptable measurement of dispersant effectiveness, the oil mass balance must be demonstrated. While this is normal in small-scale laboratory apparatus, it has not been achieved in larger-scale wave basin and open ocean experiments despite the work of a large number of research groups. Five years ago, mass balance was either ignored or losses of up to fifty percent of the applied oil were accepted. In the larger wave basin experiments, recent work has reduced the unknown losses to between ten and fifteen percent. No such reduction has been achieved in the few open ocean tests that have been recently undertaken. This paper will discuss the limitation of current measuring technologies and experimental techniques. New experimental techniques will be suggested to improve mass balance calculations, such as changing the experimental plan for large wave basins from a Lagrangian description of motion to an Eulerian frame of reference. There are technologies, that can be applied to large wave basins, that are unique to this experimental situation, but are not widely used. These technologies will be discussed. For the open ocean situation, there are seldomly-used methods, which could be employed to improve the measurement of sub-surface plume characteristics and oil thickness on water. The current limitations of these technologies will be discussed.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".