Chemical Weathering Patterns of Diluted Bitumen Spilled into Freshwater Limnocorrals
Bibliographic record
Abstract
Due to the sudden nature of oil spills, few controlled studies have documented how oil weathers immediately following accidental release into a natural lake environment. Here, we evaluated the weathering patterns of Cold Lake Winter Blend, a diluted bitumen (dilbit) product, by performing a series of controlled spills into limnocorrals installed in a freshwater lake in Northern Ontario, Canada. Using a regression-based design, we added seven different dilbit volumes, ranging from 1.5 to 180 L, resulting in oil-to-water ratios between 1:71,000 (v/v) and 1:500 (v/v). We monitored changes in the composition of various petroleum hydrocarbons (PHCs), including n -alkanes, polycyclic aromatic hydrocarbons (PAHs), and oil biomarkers in dilbit over time, as it naturally weathered for 70 days. Depletion rate constants ( k D ) of n -alkanes and PAHs ranged from 0.0009 to 0.41 d –1 and 0.0008 to 0.38 d –1, respectively. There was no significant relationship between k D and spill volume, suggesting that spill size did not influence the depletion of petroleum hydrocarbons from the slick. Diagnostic ratios calculated from concentrations of n -alkanes, isoprenoids, and PAHs indicated that evaporation and photooxidation were major processes contributing to dilbit weathering, whereas dissolution and biodegradation were less important. These results demonstrate the usefulness of large scale field studies carried out under realistic environmental conditions to elucidate the role of different weathering processes following a dilbit spill.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".