A REVIEW OF OIL-IN-WATER MONITORING TECHNIQUES
Bibliographic record
Abstract
ABSTRACT A comprehensive laboratory study of the Turner Instrument flow-through fluorometers was conducted to review their ability to measure real-time oil-in-water concentrations, to compare the results to alternative total petroleum hydrocarbon (TPH) procedures and to carry out supporting laboratory analysis in order to further understand the relationship of the fluorescent signal to the composition of the oils. The model 10 AU was equipped with the long wavelength optical kit for crude oils while the model 10 was equipped with the short wavelength optical kit for diesel fuels and light refined oil products. Eight oils and the dispersant COREXIT®9500 were used. The oils were Alberta Sweet Mixed Blend crude oil (0% and 27% weathered), Prudhoe Bay crude oil (0% and 27% weathered), Bunker C fuel oil (0% and 8% weathered), and diesel fuel (0% and 37% weathered). Efforts were made to establish a calibration procedure which was used to convert the real-time fluorometer data to oil concentration. Initial comparisons of the fluorometer results to standard infrared and gas chromatography laboratory procedures showed all methods capable of detecting and differentiating between small changes in oil concentration. The infrared and gas chromatography generated similar values while the fluorometer values were of the same order of magnitude but typically 20 to 80% higher.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".