Integrated electrical resistivity tomography (ERT) and self-potential (SP) techniques for assessing hydrological processes within glacial lake moraine dams
Bibliographic record
Abstract
Abstract Moraine dams can be inherently unstable, but effective assessment strategies remain poorly identified. We integrate electrical resistivity tomography (ERT) with electrical self-potential (SP) and lake-level measurements to investigate the structure of, and hydrological processes within, a moraine-dam complex adjacent to Miage glacier, Italy. This complex separates two meltwater lakes characterized by an efficient subterranean hydraulic connection. Our ERT data reveal a continuous free surface within the complex, whose morphology reflects the topography of the moraine complex akin to unconfined groundwater aquifers. SP data were corrected for spatial changes in the thickness of the upper unsaturated layer using principles of electrography. The residual streaming-potential map is consistent with Darcian flow of lake waters through the moraine complex, characterized by a negative-to-positive potential change from <-30 mV to >+70 mV. These electrical signatures are consistent with those generated by water seepage through earth dams in various non-glacial settings. Integrated electrical geophysical methods thus provided an inexpensive and unobtrusive evaluation of the hydrological properties of and processes within the moraine complex. Since spatio-temporal patterns of subsurface water flow critically affect the strengths of moraine sediments, such methods promise to be powerful in assessing the long-term stability of moraine-dammed glacial lakes.
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 | 0.000 | 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 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".