Monitoring of polycyclic aromatic hydrocarbon contamination at four oil spill sites using fluorescence spectroscopy coupled with parallel factor-principal component analysis
Why this work is in the frame
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Bibliographic record
Abstract
Fluorescence spectroscopy analysis of oil and environmental samples collected from four oil spill incidents in Canada-a 2016 pipeline spill into the North Saskatchewan River (NSR), Saskatchewan; a 2015 train derailment in Gogama, Ontario; the 1970 sinking of the SS Arrow ship in Chedabucto Bay, Nova Scotia; and the 1970 sinking of the Irving Whale barge in the Gulf of St. Lawrence-permitted assessment of the PAH content of environmentally weathered samples. A recently developed fluorescence fingerprinting model based on excitation-emission matrix-parallel factor analysis-principal component analysis (EEM-PARAFAC-PCA) was applied to (i) evaluate the intensity of the abundant PAH groups in the samples, (ii) investigate changes in the PAH composition of environmental samples over time due to weathering, and (iii) classify the original spilled oil and environmental samples within the already established classes of the fingerprinting PCA model. The environmental sediment samples collected from the Husky Energy spill site show loss of PAHs occurring over the course of 15 months post-spill. However, the extent of weathering depends on several environmental factors rather than solely the time of weathering, the PAH loss was maximum at 15 months. There was a decrease in the PAH content of the environmental samples of Gogama spill collected 20 months post-spill. Almost all of Gogama environmental sediment samples underwent substantial weathering, making PCA classification impractical. The SS Arrow and Irving Whale samples fell within adjacent PCA groups, as they both had a similar type of spilled oil (Bunker C) with similarity in chemical composition.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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