Evaluation of Oil-Sediment Interactions and the Possibility of Oil Sinking in Marine Environments
Bibliographic record
Abstract
ABSTRACT 2017-217 One of the major unknowns with respect to the fate and behavior of spilled dilbit is its state of buoyancy, particularly when mixed with sediments. What form do diluted bitumen and sediment mixtures take, and will they float or sink in water column? In this study, we evaluated the fate and behavior of Cold Lake Blend-Winter at three different weathering states (i.e., fresh, medium, and heavily weathered) in high-energy mixing marine conditions, with (and without) sufficient concentrations of sediments to ensure formation of oil-particulate aggregates (OPA). Conventional light crude oil and intermediate and heavy fuel oils were also included to serve as references for behavior of other types of oils. Two mineral sediments including kaolin (fine) and sand (coarse) as well as natural sediment from the Douglas Channel (DC) in northern British Columbia, (medium) were used for this evaluation. The resulting OPAs were characterized in terms of buoyancy, OPA density, particle size distribution, and morphology to better understand the oil-sediment interaction. In the absence of sediment, mixing of the oils with water resulted in meso/entrained–water-in-oil mixtures; these mixtures remained floating on the water surface. However, in presence of kaolin or the DC sediments, a significant portion of the OPA sank to the bottom of the water column for all oils, with the exception of the light crude oil and the highly weathered Cold Lake Blend-Winter. The later did not uptake as much sediment and instead formed discrete free-floating tarballs. In experiments with the larger sand sediment, no OPAs formed. The density and particle size analysis revealed that the OPAs of the oils with higher viscosity tended to have larger densities and particle sizes. Microscopic examination of the OPAs showed that all consisted of oil droplets surrounded by sediment particles and were present in single droplet or multiple-droplet clusters.
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 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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".