A Review of Oil-in-Water Monitoring Techniques: The Concluding Results
Bibliographic record
Abstract
ABSTRACT A comprehensive laboratory study of the Turner Instrument flow-through models 10AU and 10 fluorometers was conducted to review their ability to measure real-time oil-in-water concentrations and to further understand the relationship of the fluorescence to the chemical composition of the oils. The oils and dispersant used in the program were Alberta Sweet Mixed Blend (ASMB) crude oil (0% and 26% weathered samples), Prudhoe Bay (PB) crude oil (0% and 27% weathered samples), Bunker C (BC) fuel oil (0% and 8.4% weathered samples), Diesel fuel (0% and 37% weathered samples) and Corexit 9500 respectively. The chemical composition of the oils was determined by gas chromatographic techniques and compared to the signal outputs of the fluorometers. It was found that the fluorometer data could not be directly linked to the concentration of any specific aromatic hydrocarbon such as naphthalene or to the sum of the polycyclic aromatic hydrocarbon (PAH) compounds. Evidence suggests that the fluorescence signal is generated by a combination of PAH compounds. The relative contribution of each PAH compound is not equal. Finally, the response of the fluorometers may also be influenced by the presence of volatile aromatic compounds such as BTEX and C3- benzenes in combination with the PAH compounds.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".