Fate of Surface Spills of Cold Lake Blend Diluted Bitumen Treated with Dispersant and Mineral Fines in a Wave Tank
Bibliographic record
Abstract
Cold Lake Blend (CLB) diluted bitumen (dilbit) was used to evaluate the fate and transport of preweathered (6.2% w/w) dilbit under environmental conditions both in spring (seawater temperature 8.5°C±1.3°C and salinity 27.7±1.6 practical salinity units [psu]) and in summer (seawater temperature 17.0°C±2.6°C and salinity 26.8±2.4 psu). The following oil spill treatments were considered: no treatment, dispersant alone, mineral fines (MF) alone, and dispersant+MF. The aim was to determine their influences on the fate of spilled CLB at sea. When dispersant alone was used, the highest dispersion effectiveness (DE) was noted, and DE ranged from 45% to 59% under the selected environmental conditions. With no treatment and treatment of MF alone, CLB DE was insufficient under tested conditions. Total petroleum hydrocarbon (TPH) concentration in the water column was highest for the dispersant alone, followed by that of dispersant+MF. TPH concentration for the dispersant alone increased abruptly with time. Droplet size distribution (DSD) resulting from dispersant alone had a unimodal shape, which was different than previously observed when conventional oils were treated with the dispersant. Cases of dispersant+MF were thus characterized by a broader DSD compared with dispersant only and a gradual increase in TPH concentration. This suggests that MF could be used with dispersant as a means to control the release of toxic compounds into the water column and for better engineering the response.
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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) | 0.001 | 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".