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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".