EFFECTS OF CHEMICAL DISPERSANT ON OIL SEDIMENTATION DUE TO OIL-SPM FLOCCULATION: EXPERIMENTS WITH THE NIST STANDARD REFERENCE MATERIAL 1941?
Bibliographic record
Abstract
ABSTRACT As it is well established that application of chemical dispersant to oil slicks enhances the concentration of oil droplets and reduces their size, chemical dispersants are expected to enhance oil sedimentation if applied in coastal waters rich in suspended particulate matter (SPM) and if flocculation between chemically dispersed oil and SPM, which leads to formation of oil-SPM aggregates (OSAs), occurs readily. New laboratory experiments were conducted to establish a quantitative understanding of the process and to verify this hypothesis. This paper presents findings from experiments conducted using Standard Reference Material 1941b prepared by the National Institute of Standards and Technology, Arabian Medium, Alaska North Slope and South Louisiana crude oils, and Corexit 9500 and Corexit 9527 chemical dispersants. Results showed that OSAs do form with chemically dispersed oil. Oil sedimentation increases with sediment concentration and reach a maximum at a sediment-to-oil ratio of approximately 2:1 for most of the oils used. No obvious effect of chemical dispersant on oil sedimentation was measured for sediment concentration of 100 mg/L and higher. However, measured oil sedimentation was 3 to 5 times higher with chemical dispersant than with physically dispersed oil at low sediment concentration of 25 and 50 mg/L. UV epi-fluorescence microscopy showed that OSAs formed with chemically dispersed oil contain many oil droplets that are smaller than those trapped in OSAs formed with physically dispersed oil.
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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 | 0.001 | 0.002 |
| 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".