PRESENCE AND DISTRIBUTION OF POLYCYCLIC AROMATIC HYDROCARBONS IN SEDIMENT CONTAMINATED WITH TAR SANDS CRUDE OIL
Bibliographic record
Abstract
Contamination of sediment with polycyclic aromatic hydrocarbons (PAHs) derived from heavy crude oils (ex. tar sands oil) pose significant threats to human health as well as to the natural ecosystem. These compounds may persist in the environment for long periods of time following a crude oil spill. As such, this study sought to evaluate the persistence of PAHs in sediment and possible correlation between PAH distribution and grain size. This was accomplished through the collection of sediment samples from a portion of river bank along the Kalamazoo River near Ceresco, MI. Five years previously, a pipeline break spilled an estimated 843,000 gallons of diluted bitumen being transported from Alberta, Canada’s Athabasca oil field. Samples were collected from two areas: 1) an area of the river bank that was reworked following the spill and 2) an area in the floodplain thought to have been inundated with oil at the time of the spill but has since remained relatively undisturbed. The samples were analyzed using gas chromatography-flame ionization detection (GC-FID) for 17 PAHs known to have potentially harmful human and ecosystem health effects. Results indicate the presence of PAHs in all samples, including individual compounds which can be used as biomarkers for the Athabasca oil field. In addition, a detailed analysis of grain size was carried out on each sediment sample. There is some variability in the presence of specific PAHs between sample location and sediment grain size fraction, though identifying a clear correlation is complex. Since production and transportation of tar sands oil is projected to increase in the coming years, understanding the fate of PAHs in the environment is crucial to remediation preparedness. By relating the persistence of PAH compounds to grain size in a dynamic natural environment, it may be possible to better predict areas where PAHs may concentrate in future spills of tar sands oil, thus better informing future remediation efforts in similar environments.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".