Characteristics of Oil Droplets Stabilized by Mineral Particles: The Effect of Salinity
Bibliographic record
Abstract
ABSTRACT The influence of salinity on the characteristics of oil droplets stabilized by mineral particles (oil-mineral aggregates – OMA) was studied in the laboratory using three different oils and a natural sediment. Size and concentration of oil droplets associated with negatively and positively buoyant OMA were measured by image analysis using epi-fluorescence microscopy. Results showed that the median droplet size increases rapidly from about 5 μm at zero salinity to double at salinity close to 1.2 ppt; decreases dramatically to about 5 μm at salinity 3.5 ppt and then increases slightly to 6 μm at the seawater salinity. The concentration of oil droplets also increases sharply when the salinity increases from zero to a critical aggregation salinity Scas, after which it stabilizes at its maximum value. The concentration of mineral-stabilized droplets is strongly affected by oil type at any salinity. When normalized to its maximum value, the concentration of droplets correlates well with normalized salinity S/Scas. A relationship is derived to predict the effect of salinity on the concentration of mineral-stabilized droplets. Size distributions of oil droplets follow similar trends, but their magnitudes depend on salinity and oil type. Self-similarity in droplet size distributions was shown when the data were plotted using normalized variables N/Nt and D/D50, where N is the number of droplets of diameter D, Nt is the total number of droplets and D50 the median size of the droplets. With these normalized variables, oil droplet size distributions measured in this study and those measured in field and laboratory under various conditions by different investigators fit the same curve regardless of the formation conditions of the droplets. A function is derived to calculate normalized cumulative size distributions of oil droplets.
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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) | 0.000 | 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".