COMBINING LAND AND WATERBORNE ELECTRICAL RESISTIVITY TOMOGRAPHY FOR IMPROVED INFRASTRUCTURE PLANNING ON WATERWAYS
Bibliographic record
Abstract
When preparing for an infrastructure project near or beneath a waterway it is important to have access to information about geological structures and materials that will influence the project’s design and construction. In particular, knowledge of the thickness and extent of the unconsolidated granular deposits that typically occur under waterways is crucial to the design of foundations and horizontal direction drilling (HDD) routes that cross waterways. Land based electrical resistivity tomography (ERT) and seismic refraction provides accurate information about geological structures on either side of waterways, but if survey cables cannot be strung across a waterway it is difficult to obtain information about materials and structures beneath the waterway itself. Using a waterborne ERT system, it is possible to collect high quality, continuous ERT data over waterways, which complement land-based ERT data. In the past year, this method has successfully aided in the planning of multiple infrastructure projects in Canada. In the first project, waterborne ERT was combined with land-based ERT and seismic refraction for planning two natural gas pipelines in Canada. In order to optimally locate HDD paths under rivers both land based and waterborne ERT were used to delineate bedrock and to locate zones of unconsolidated sand and gravel beneath rivers. In a second project waterborne ERT was combined with land-based ERT at potential water intake sites along a river in northern BC. The purpose of the program was to delineate alluvial gravels and the underlying bedrock unit on shore and beneath the river bed. Using this technique, it was possible to delineate geologic units beneath the river and along the banks, providing valuable inputs for project planning.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".