Application of electrical resistivity imaging to the development of a geologic model for a proposed Edmonton landfill site
Bibliographic record
Abstract
Electrical resistivity imaging (ERI) was used to further characterize the geologic setting at the proposed Aurum solid-waste landfill site near Edmonton, Alberta. Two bedrock channel aquifers, the east and south channels, exist in or near the site. Previous studies at the proposed site used borehole and pumping test data to determine that the two aquifers are separate and hydraulically disconnected by a sheet of ice thrust bedrock. The three objectives of the ERI were to resolve the sand channels and terrace sands, resolve the top of the thrust bedrock, and resolve the sand channels beneath the thrust bedrock. The ERI survey, in combination with the borehole data, presented a more detailed representation of the site's complex geology than borehole data alone. The south channel has a long and even bottom, steeply sloping sides, and two levels. The thrust bedrock occurs as irregular massive blocks throughout the site, and the aquifers are hydraulically confined by the glacial till. The bedrock surface is highly variable where it has been modified by glacial ice thrusting and relatively uniform elsewhere. Electrical resistivity imaging was found to be useful for the prompt and accurate development of a geologic model for the proposed Aurum landfill site.Key words: electrical resistivity imaging, thrust bedrock, site characterization.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".