PARTICLE SIZE ANALYSIS OF DISPERSED OIL AND OIL‐MINERAL AGGREGATES WITH AN AUTOMATED ULTRAVIOLET EPI‐FLUORESCENCE MICROSCOPY SYSTEM
Bibliographic record
Abstract
This paper describes recent advances in microscopic analysis for quantitative measurement of oil droplets. Integration of a microscope with bright-field and ultraviolet epi-fluorescence illumination (excitation wavelengths 340-380 nm; emission wavelengths 400-430 nm) fitted with a computer-controlled motorized stage, a high resolution digital camera, and new image-analysis software, enables automatic acquisition of multiple images and facilitates efficient counting and sizing of oil droplets. Laboratory experiments were conducted with this system to investigate the size distribution of chemically dispersed oil droplets and oil-mineral aggregates in baffled flasks that have been developed for testing chemical dispersant effectiveness. Image acquisition and data processing methods were developed to illustrate the size distribution of chemically dispersed oil droplets, as a function of energy dissipation rate in the baffled flasks, and the time-dependent change of the morphology and size distribution of oil-mineral aggregates. As a quantitative analytical tool, epifluorescence microscopy shows promise for application in research on oil spill response technologies, such as evaluating the effectiveness of chemical dispersant and characterizing the natural interaction between oil and mineral fines and other suspended particulate matters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".