Petroleum contamination characterization and quantification using fluorescence emission-excitation matrices (EEMs) and parallel factor analysis (PARAFAC)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper introduces a novel approach to characterize and semi-quantify common petroleum contaminants (natural gas condensate, gasoline, diesel, flare pit residue, and heavy crude oil) and their underlying aromatic hydrocarbon components in solutions based on their fluorescence spectral signatures. The method uses fluorescence excitation-emission matrices (EEMs) combined with multivariate statistical procedures: parallel factor analysis (PARAFAC) and soft independent method of class analogy (SIMCA) to identify the petroleum products. Quantitatively, fluorescence intensities of EEMs of analyzed petroleum products at different concentrations are used to establish standard calibration curves that can be employed to estimate unknown concentrations of similar petroleum products in solutions. As well, underlying aromatic hydrocarbon component concentrations are estimated by performing customized PARAFAC analysis. This approach provides fingerprints for different petroleum products along with estimates of their concentrations in non-fluorescing solvents. Concentrations of predicted PARAFAC components were validated by laboratory chemical analytical results of the same petroleum products.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it