Detection of Crude Oil Contamination in St-Lawrence Estuary Sediments Using n-Alkanes and PAHs Diagnostic and Isotopic Ratios
Bibliographic record
Abstract
Excessive consumption of petroleum and crude oil for energy purposes has resulted in the contamination of many natural systems and waterways. However, determining the presence and level of contamination has been difficult due to the presence of naturally occurring hydrocarbons and to the complexity of the molecular fingerprint of petroleum and crude oils. Naturally occurring straight-chain n-alkanes and polycyclic aromatic hydrocarbons (PAHs) in sediments are both commonly used to determine organic matter sources through diagnostic and isotope ratios, and these ratios are affected by the presence of petroleum and crude oil. As such, they offer a potential avenue for determining whether crude oil contaminants are present in natural systems. The purpose of this project was to determine whether diagnostic ratios of n-alkanes and of PAHs as well as compound-specific isotope ratios of n-alkanes (∂2Halk and ∂13Calk) could be used to detect crude oil or petroleum contamination, and at what level of contamination the difference becomes significant. This was accomplished by separating the aliphatic and aromatic fractions of the natural and crude oil hydrocarbons by column chromatography, spiking natural sediment hydrocarbons with crude oil hydrocarbons at different levels, and analyzing the samples by GC-MS (Gas Chromatography-Mass Spectrometry) and by GC-IRMS (Isotope-Ratio Mass Spectrometry). The isotopic ratios and the hydrocarbon concentrations were determined by external standard calibration, and the diagnostic ratios were then calculated from the concentrations. Both ratios were then evaluated for their efficiency in detecting the presence of crude oil contamination.
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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 | 0.000 | 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 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".