Inter-laboratory calibration of estimated oil equivalent (EOE) concentrations of a water accommodated fraction (WAF) of oil and a chemically enhanced WAF (CEWAF)
Bibliographic record
Abstract
Chemical characterization of the presence of oil in environmental samples are performed using methods of varying complexity. Extraction of samples with an organic solvent and analysis by fluorescence spectrometry has been shown to be a rapid and effective screening technique for petroleum in the environment. During experiments, rapid analysis of oil by fluorescence provides the opportunity for researchers to modify the experimental conditions in real time. Estimated Oil Equivalents (EOE) relies on the fluorescence measurement of the aromatic compounds to estimate the oil concentration. The present intercalibration study was designed to investigate whether different fluorometer instruments can reliably measure EOE and whether the results are intercomparable. Additionally, the need for extraction of oil compounds into an organic solvent was investigated. Three different fluorometers were used in three different laboratories: a Horiba Aqualog, a Turner Trilogy and a Shimadzu Spectrofluorophotometer RF-1501. Results from these different instruments showed excellent agreement for EOE determinations. A very high correlation was found between the EOE results obtained with Aqualog Horiba and Turner Trilogy (r 2 = 0.9999), with no significant differences between the mean EOE results (t-test, p=0.30), and the Aqualog Horiba and Shimadzu (r 2 = 0.995) fluorometers, with no statistically difference between the EOE results obtained by the two instruments (p = 0.40).
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".