The fate and behavior of petroleum biomarkers in diluted bitumen and conventional crude oil exposed to natural sunlight in simulated seawater
Bibliographic record
Abstract
Given that the physicochemical properties of diluted bitumen (dilbit) can differ from those of conventional crude oil, understanding the fate and behavior of this petroleum product in the environment becomes vital. This study involves the analysis of the photolytic behavior of some representative petroleum biomarkers, bicyclic sesquiterpanes (BSs), admantanes (ADs), diamantanes (DAs), and mono- and triaromatic steranes (MASs and TASs), by exposing Cold Lake Blend (CLB) and Alberta Sweet Mixed Blend (MSW) to winter and summer insolation after being spilled onto artificial brines. Aromatic steranes in all control samples remained relatively stable, whereas the biomarkers of BSs, ADs, and DAs were less stable. Similar to the exhaustive loss of the C10–C17 alkanes, 91%–99% of BSs, ADs, and DAs were lost after five days of insolation, especially in summer. Both MASs and TASs were lost gradually in most scenarios, although both of them were lost faster in MSW than observed for CLB. The removal of MASs and TASs did not differ significantly from each other, although their loss was less than observed for PAHs having similar number of rings and greater than for the C21–C33 n-alkanes. Therefore, photooxidation, not evaporation or biodegradation, was the main factor responsible for oxidizing these aromatic steranes. However, biomarkers of BSs, ADs and DAs were mostly lost through evaporation. Therefore, aromatic steranes have the potential to be utilized to evaluate the photolytic behavior of petroleum hydrocarbons, while BSs, ADs, and DAs should not be used for this purpose.
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".