A Comparison of Electrical Resistivity, Ground Penetrating Radar and Seismic Refraction Results at a River Terrace Site
Bibliographic record
Abstract
Abstract Electrical resistivity imaging (ERI), ground penetrating radar (GPR) and seismic refraction (SRF) profiles were repeated over three lines on a terrace of the Bow River. The site had a resistive gravel layer overlying mudstone bedrock with horizontal transitions to lacustrine and overbank deposits. Electrical resistivity results were best for determining changes in sediment types and detecting boundaries, but the ERI smoothness constraint blurred the location of the boundaries. The GPR gave the most resolution and showed internal structures that the other methods did not image. The GPR signal was severely attenuated in several areas where the surficial sediments became too conductive because of a fine grained component. The seismic refraction inversion provided good reproduction of the bedrock interface, but it did not detect changes in the composition of the surficial sediments. It also required the introduction of a low velocity surficial layer not indicated by the other methods that may be related to the increase in effective stress with depth. Jointly interpreting the three data sets gives a more reliable and less ambiguous interpretation than any single method. The data may be useful to test joint inversion algorithms and are available for download.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".