Using molecular, isotopic, and spectroscopic analysis to assess natural organic matter sources and petroleum contaminants in water and sediment of the St. Lawrence Waterway (Quebec, Canada)
Bibliographic record
Abstract
The St. Lawrence waterway is a dynamic aquatic system with inputs of organic matter (OM) originating from various terrestrial and marine sources, and a freshwater to saltwater gradient from the St. Lawrence River to the Gulf. The OM along this waterway is highly reworked in the water column, with a small fraction making its way into the sediments which acts as a long-term sink for organic matter. This waterway is also an important transportation route for numerous commodities, including petroleum and petroleum products. Our research involved the comprehensive mapping of the OM in the sediments and water column of the St. Lawrence River, Estuary, Gulf and the Saguenay Fjord. Water samples were collected along the River, Estuary, Gulf and Saguenay Fjord and parallel factor analysis was used to tease out various groups of fluorophores. In addition to the natural water samples collected along the St. Lawrence waterway, water samples containing UV irradiated petroleum products were included in the PARAFAC model to determine its efficiency at teasing out the components originating from natural OM from those linked to oil contamination. The PARAFAC analysis resulted in the identification of 6 components in our data set, with 4 components indicative of natural organic matter (3 terrestrial OM and one marine OM) and 2 representing oil components. With these findings, we were able to characterize groups of fluorophores along this transect and develop a ratio using 2 components (C4/C1) to differentiate oil contributions from natural OM in the water column. Similarly, surface sediments along the St. Lawrence Estuary, Gulf and Saguenay Fjord were collected and extracted to isolate the straight-chain n-alkanes to map the current abundances and sources of hydrocarbons in sediments of the Estuary and Gulf using molecular (diagnostic ratios) and isotopic fingerprinting (δ13C, δ2H). Variations in the carbon isotope signatures of odd-to-even straight chain alkanes allows for the differentiation of naturally occurring hydrocarbons from those of petroleum source, and the addition of hydrogen isotope signatures further increases our power of discrimination. Based on the diagnostic ratios alone, the OM sources were misrepresented and inaccurate when there was more than one input of OM. However, with the addition of the compound specific carbon and hydrogen isotope analysis, it was determined that n-alkanes were derived predominantly from natural sources. Additionally, we found that even numbered n-alkanes, which are less frequently analyzed due to their lower abundances in natural samples, would allow for the identification and tracking of petroleum-derived contaminants in sediments to a greater degree than molecular data alone. Analyzing both the possibility of oil contamination in the water column and sediments allows for the tracking of recent and long term impacts an oil spill would have along the St. Lawrence River, Estuary Gulf and Saguenay Fjord.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".