Electrical resistivity and surface-wave methods for the detection of shallow objects in glaciolacustrine clay
Why this work is in the frame
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Bibliographic record
Abstract
Geotechnical site characterization is typically accomplished through the use of drilling programs, frequently employing numerous bore holes to characterize the subsurface at discrete locations. While usually sufficient, this method of investigation can be problematic for highly heterogeneous subsurface conditions, as the approach generally lacks lateral resolution. This deficiency in horizontal resolution can be particularly damaging in the Winnipeg and surrounding area, as the near-surface stratigraphy includes a thick sequence of glaciolacustrine clay that often contains sporadic collections of glacial debris. Such collections, if left undetected, can potentially result in engineering project delays and associated cost overruns. To overcome such problems, and provide an alternate method of subsurface characterization, the feasibility of using both electrical resistivity tomography (ERT) and multi-channel analysis of surface waves (MASW) geophysical methods for the detection of buried glacial debris was investigated. To assess the suitability of each method, testing was completed in two main stages, with both numerical modeling and field analysis conducted. During the initial modeling stage, results indicated that both methods were capable of detecting larger debris accretions (lateral extent > 8m), with each recovering a response for such objects at depths upwards of 6 m. Results from subsequent field testing were similarly successful, with each method detecting a response from buried debris of various size and depth. ERT data was found to be particularly effective, with data from field testing accurately recovering the lateral position of numerous discrete objects. In comparison, while successful at detecting an anomalous response, the MASW method was unable to image singular objects, with the signature from individual features being averaged into a single layer. However, secondary backscattering analysis (BSA) completed using the same seismic data proved successful for the detection of discrete objects, with results providing both an accurate lateral location and depth of each.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it