ULTRAVIOLET FLUORESCENCE SPECTROSCOPY (UVFS): A NEW MEANS OF DETERMINING THE EFFECT OF CHEMICAL DISPERSANTS ON OIL SPILLS
Bibliographic record
Abstract
ABSTRACT Crude oils dispersed in seawater produce distinct emission spectra when exposed to ultraviolet (UV) light. The spectra can be used to estimate how effectively oil is dispersed by chemical methods. Oil dispersants (such as Corexit 9500) have a pronounced effect on water-based UV spectra, strongly enhancing emission at 445 nm. This enhancement of fluorescence over the 455 nm bandwidth is the result of dispersant breaking up higher molecular weight (>3 ring) polycyclic aromatic hydrocarbons (PAHs) into stable suspensions of small droplets. Ultraviolet fluorescence spectroscopy (UVFS) has been tested as a rapid analytical tool in the laboratory and in a wave tank designed to investigate the response of crude oils to dispersants and a range of energy dissipation rates. The results obtained with UVFS are consistent with standard chemical analyses, confirming that the method can be employed as a rapid, quantitative measure of dispersed oil concentration. Given that higher molecular weight PAHs are associated with many of the persistent toxic effects of crude oils on marine organisms, UVFS may also prove to be a useful tool for tracking these fractions during dispersed oil toxicity assays.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".