Soil type effects on petroleum contamination characterization using ultraviolet induced fluorescence excitation-emission matrices (EEMs) and parallel factor analysis (PARAFAC)
Bibliographic record
Abstract
The ultraviolet induced fluorescence signatures of various petroleum products were evaluated in different soils to examine the impact of soil type, grain size, porosity, and mineralogy. The different soil matrices induced changes to the spectral features of petroleum hydrocarbon fluorescence excitation-emission matrices (EEMs). Once the effect of the soil matrix was characterized, fluorescence EEMs were analyzed using parallel factor analysis (PARAFAC) and soft independent method of class analogy (SIMCA) to identify the petroleum products and their underlying aromatic hydrocarbon components. For quantitative analysis, total fluorescence values obtained from fluorescence EEMs of analyzed petroleum products were used to estimate their concentrations in different soil matrices. Results indicated that this approach provides identifying fingerprinting and reasonable estimate of concentrations for a number of petroleum products in different soils matrices.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".