Effects of Sub-Aerial Weathering on Hydrocarbon Distributions in Oil Sands (Athabasca Oil Sand, Canada and Parana Basin, Brazil)
Bibliographic record
Abstract
Sub-aerial weathering plays a significant role in the reduction of low molecular weight hydrocarbons in surface exposed oils. In order to investigate the detailed effects of sub-aerial weathering as opposed to sub-surface biodegradation, a suite of ten (10) oil sand samples were solvent extracted and analyzed in the laboratory using liquid column chromatography separation, gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). The gas chromatograms showed total loss of n-alkanes and isoprenoids while the ion mass chromatograms showed alternation in steranes, little loss of hopanes and no pronounced loss of higher molecular weight of triaromatic steroids; thus, they were at between levels 5-8 on the Peters and Moldowan (PM) scale of biodegradation. GC-MS analysis was performed to analyze biological markers. Among the biological markers, the most important and noticeable effects were a decrease in the 20S/20R diastereomer ratio of the C29 steranes. There were also evident effects on the overall distributions and abundance of pentacyclic terpanes thus, leading to alteration of biomarker parameters which could result in erroneous interpretation of source depositional environments, source correlation and maturity.
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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 | 0.000 | 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) | 0.000 | 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".