Geophysical Characterization of an Undrained Dyke Containing an Oil Sands Tailings Pond, Alberta, Canada
Bibliographic record
Abstract
Abstract Geophysical characterization of an undrained oil sands tailings pond dyke was conducted at Syncrude Canada’s Southwest Sand Storage Facility (SWSS). Push tool conductivity (PTC), electromagnetic (EM), and electrical resistivity tomography (ERT) methods in tangent with hydrogeological and chemistry measurements were used to investigate soil moisture, hydraulic heads, and groundwater salinity distributions. Geophysical data were collected from 2001 to 2008 and interpretations can further be used to validate studies of groundwater flow and salt transport within the structure. An Archie’s Law petrophysical model was used to relate measured bulk conductivity, from geophysical surveying, with measures of soil moisture and fluid electrical conductivity. It was found that a relatively strong relationship between bulk electrical conductivity and soil moisture exists, while weak to no correlation was observed between bulk and fluid electrical conductivity. ERT surveying was capable of clearly identifying the location of the capillary fringe within the dyke. This study provides a unique look into the application of geophysical techniques to investigate soil moisture, hydraulic head, and salt distribution in an active undrained tailings dam structure. The methodology and insights gained from this study may be applied to similar undrained and drained oil sands tailings storage sites.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".