A protocol for assessing the effectiveness of electrical resistivity imaging for agricultural dike investigation
Bibliographic record
Abstract
DC electrical resistivity surveying has shown much promise for investigating dikes and other earthen flood barriers. We are interested in the applicability of such data for aiding with maintenance and construction efforts in the Tantramar region of New Brunswick and Nova Scotia, Canada, where agricultural dikes form an important part of critical flood prevention infrastructure. Specifically, our goal is to develop efficient field survey and data processing protocols for detecting possible internal issues in the dikes ahead of further, more detailed geophysical surveying. The field survey protocol must be cost and time effective, given the large lengths of dikes that must be surveyed. The Tantramar dikes are expected to exhibit strong subsurface heterogeneity but accurately characterizing their internal structure may be challenging. Dikes have significant 3D geometry and traditional 2D DC surveying, and subsequent 2D inversion, fails to provide reliable and interpretable results. 3D surveying and inversion may be required but this represents significantly higher field costs. We performed a detailed synthetic inverse modelling study to help design our field surveying protocols. We used a representative model of a dike in the Tantramar region and we worked with the specifics of the surveying equipment available to us. We investigated and compared three possible data acquisition layouts proposed by other authors, we thoroughly compared the results of 2D versus 3D inversion on those layouts, and we performed a detailed investigation to assess best practices for 3D inversion mesh design. We are also incorporating joint interpretation with EM data, collected using mobile survey devices such as the Geonics EM31. Results from synthetic forward and inverse modelling are helping us develop future field data collection, processing and modelling protocols.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".