An evaluation of electrical resistivity imaging (ERI) in Quaternary sediments, southern Alberta, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 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 it