Sediment Contamination Due to Oil-Suspended Particulate Matter Aggregation during Oil Spills in Coastal Waters
Bibliographic record
Abstract
Aggregation between suspended oil droplets and suspended particulate matter (SPM), which leads to the formation of oil-SPM aggregates (OSAs), is recognized as an important process affecting the fate of spilled oil in fresh and marine water systems. It affects oil sedimentation and; thus, contamination of bottom sediments during oil spill events. This paper presents laboratory results from a multi-year research project to gain quantitative understanding of the factors controlling the formation and fate of OSAs formed with naturally and chemically dispersed oils. The results relate to the measurements of OSA's content in total petroleum hydrocarbon (TPH), size distribution, density and settling velocity. Oil sedimentation caused by negatively buoyant OSAs varied from 0.3 % to 56 %. The highest percentage of oil sedimentation was obtained with chemically dispersed oil. The size of OSAs varied from 40 to 700 μm. The median size varied between 115 and 240 μm. The effective density and settling velocity varied between 10 and 200 g/L and 0.3 and 3 mm/s, respectively. The study showed that sediment grain size and concentration have strong influence on OSA formation. For a relatively low sediment concentration of 100 mg/L, OSA formation can lead to significant enhancement of oil transfer from the water surface to bottom sediments. Overall, the study showed that the formation and physical properties of OSAs are similar to those of sediment flocs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".