The Effect of Varying Salinity and Temperature on the Dynamics of Orimulsion in Water
Bibliographic record
Abstract
ABSTRACT Studies have shown that Orimulsion behaves somewhat predictably in saltwater (33 ppt NaCl) and freshwater, driven by buoyancy to rise in saltwater and sink in freshwater, but behaviour in brackish water (20 ppt NaCl) is difficult to predict. Temperature has also been indicated as having an influence on Orimulsion behaviour. The current study extended experimentation to lower temperatures and a large number of salinity values, ranging from fresh to saltwater. This study resulted in information on the behaviour of Orimulsion spills in salt, fresh, and brackish water with salinity values ranging from 0.1 to 33 °/oo at temperatures of 5 and 15 °C. Depletion rates and characteristics were determined by adding Orimulsion to a 300-L tank of water, taking a time series of samples, and determining the concentration of bitumen and the particle size distribution. Changes in bitumen concentration and particle size distribution as a function of time were also measured. Resurfaced bitumen was scraped from the top of the tank and weighed to determine the amount rising. Using these data, simple equations were developed to describe and predict the concentration of bitumen in the water column as a function of time. Similarly, nomograms showing the amount of oil on the bottom and on the water surface are presented.
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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) | 0.000 | 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) | 0.001 | 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".