An evaluation of electrical resistivity imaging (ERI) in Quaternary sediments, southern Alberta, Canada
Bibliographic record
Abstract
The ability to characterize the geometry and lithology of Quaternary sediments is important to scientists who investigate groundwater movement, geoarchaeology, materials prospecting (e.g., gravel), environmental contamination and remediation, and paleoenvironmental studies. Often these studies are restricted by the limited information attainable via traditional geomorphological techniques. While there are geophysical methods for gaining information about the near-subsurface, such as ground penetrating radar (GPR) or shallow seismic surveys, they only function well under select conditions. Electrical resistivity imaging (ERI) can quickly produce high-resolution images of the shallow subsurface under many fi eld conditions. ERI measurements work well in both resistive sediments, such as gravels and sands, as well as conductive sediments like silt and clay. Resistivity is an inherent property of all materials, and it measures the degree to which a material resists the fl ow of electrical current. If a current is introduced into the ground, the resulting electrical fi eld can be measured. Thus, a two-dimensional cross section can be produced showing the resistive properties of a sediment package several meters behind an exposure. This aids in the interpretation of the material and structural features that may be present but not exposed. This methodology is successful in imaging some subsurface architecture, but there are limitations to the resolution of the surveys. ERI, when integrated with detailed geomorphologic analysis, provides enhanced insight for inferring the processes of sediment emplacement and deformational processes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".