Development and validation of a low-cost electrical resistivity tomographer for soil process monitoring
Bibliographic record
Abstract
This paper presents the development and validation of a low-cost electrical resistivity tomography (ERT) system for the monitoring of process in soils. The ERT technique is capable of evaluating the distribution of electrical properties within a cross section of soil specimens by measuring the applied electrical current and monitoring the voltage distribution along boundary electrodes. This new ERT system consists of (i) a two-dimensional (2D) acrylic testing cell with strip copper electrodes, (ii) an acquisition system and control software, and (iii) a third-party reconstruction software. The tomographer is developed to collect low-noise data at moderate acquisition speeds. To validate the quality of the proposed ERT system, noise levels were evaluated with and without the testing cell and imaging tests were conducted to calibrate the system, evaluate the sensitivity of the instrument to locate a low-resistivity inclusion, and to monitor chemical diffusion in a saturated Ottawa sand specimen. Results show that the proposed low-noise, low-cost ERT acquisition system can be successfully used, with proper interpretation, to locate inclusions and obtain images of diffusion processes in soils. These validation tests show the applicability of the developed acquisition system in monitoring time-dependent processes that do not require real-time acquisition and reconstruction.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".