GEOPHYSICAL METHODS AS AN AID TO PLANNING, MONITORING, AND ABANDONING TAILINGS FACILITIES IN THE ALBERTA OIL SANDS
Bibliographic record
Abstract
Abstract The oil sands tailings ponds of Northern Alberta have become the centerpiece of environmental opposition to unconventional hydrocarbon development in Western Canada. Significant efforts are being made to moderate the impact of oil sands tailings and tailings impoundments. Both surface and borehole geophysical techniques have a wide range of cost-effective applications to all stages of oil sands tailings management. When siting tailings impoundments, surface geoelectical methods are particularly effective in delineating Quaternary channels that, if not fully and accurately mapped, may provide pathways for off-site leachate migration. Rapidly applied waterborne geophysics are applied in much of the Athabasca watershed to establish baseline conditions before pond construction, especially regarding flow conditions resulting in naturally occurring saline discharge zones from deep seated Devonian brines. During the life of a tailings pond, surface techniques are used for imaging the accumulation of underlying “beach” sands, the varying thickness of stratified layering within tailings, and the accumulated thickness of capping material. Borehole geophysical sondes are logged directly into tailings ponds for monitoring physical properties. Where tailings sand impoundments are being reclaimed, a wide variety of surface geoelectric methods are used to image salt migration and groundwater flow; this is particularly relevant as high salinity discharge zones often function as primary controls of revegetation. Given the large area extent of ground disturbance from oil sands tailings, geophysical monitoring will continue to play a significant role in all phases of tailing management.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".