MOVEMENT AND EROSION OF ALBERTA BITUMEN ALONG THE BOTTTOM AS A FUNCTION OF TEMPERATURE, WATER VELOCITY AND SALINITY
Bibliographic record
Abstract
Abstract While many trajectory models exist to predict the movement of oil floating in or on water, few are designed to address heavy oil on the bottom of water bodies. In addition, remobilization (erosion) of the material into the water column is also difficult to predict. While properties such as adhesion, viscosity and density of oil may be readily measured, the critical shear stress (CSS) and the effect of (current) velocity, salinity, and temperature are virtually unknown for most heavy oils. The Coastal Response Research Center (CRRC) has a 4,000 L annular flume, with a water depth of 0.43 m. An inner rectangular flume (1.2 m length, 0.2m width, 0.9 m height), placed inside the annular flume, was preceded by two flow straighteners to reduced turbulence and produce a uniform, one dimensional flow field. The current is generated by an electric thrust motor and measured in 3D by a Nortek AS (Norway) Vectrino II Profiling Velocimeter. A 20g circle of Alberta bitumen (API ~ 10°) was placed on a laminated grid (1cm2 square pattern) at the bottom of the straight flume. A total of 2.3m3 of water was then gradually added to the flume. The electric motor was started and the profiler began collecting data. Two cameras, placed along the side and above the oil, collected video of the erosions and length/width changes of the oil. Conditions were held steady for one hour once the desired current velocity was achieved. Temperatures, current velocity (X, Y, Z), and digital videographic data were collected during each run. Erosions and percent lengthening of the oil was monitored as a function of water temperature, salinity and velocity. The turbulent kinetic energy (TKE) method was used to calculate the bed shear stress (BSS). In addition to the expected impact of higher temperature on the movement along the bed and erosion into the water column, the viscoelastic and shear-thinning properties of the bitumen played a role in its behavior (lowering of viscosity at higher BSS slowing erosions and movement) and must be considered when predicting its behavior during a spill.
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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 | 0.001 |
| 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".