Characterizing Dispersion Effectiveness at Varying Salinities
Bibliographic record
Abstract
ABSTRACT Chemical dispersant formulations typically provide maximum oil dispersion in waters between 30–40 ppt (parts per thousand) salt content, which encompasses typical ocean salinity (~34 ppt). As a result, most laboratory studies of oil dispersion effectiveness (DE) are conducted at low to average ocean salinity. Ocean salinity can vary locally from below 20 ppt during ice and snow melt, to extremely high (over 100 ppt) during freeze up periods or within natural brine pools in deeper waters. In this study, the influence of salinity on DE was evaluated using the baffled flask test (BFT) at a dispersant-to-oil ratio (DOR) of 1:25. Benchtop experiments were conducted with Alaskan North Slope (ANS) crude oil in the presence or absence of chemical dispersant at 5 and 25°C and varying salinities (0.2 to 125 ppt). In addition to DE as determined by BFT, oil droplet size distribution (DSD) and fluorescence intensity was measured via a LISST-100X particle size analyzer (Sequoia Scientific, Inc., Bellevue, WA) and ECO fluorometer (Sea Bird - WET Labs, Inc.; Philomath, OR), respectively. Results indicate that in the presence of dispersant, maximum DE occurred at 25ppt, and decreases above and below this salinity. Concentration of small droplets (<10 μm) was twice as high at 35ppt than at the other salinities in the presence of dispersant at 25°C. Treatments without dispersant did not vary significantly as a function of salinity. Flume tank experiments over a range of salinities support the lab scale results of DSD. These results provide a more comprehensive picture pertaining to the influence of salinity on dispersant usage at high salinities.
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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".