Airborne mapping of sensitive clay—stretching the limits of AEM resolution and accuracy
Bibliographic record
Abstract
ABSTRACT Due to postglacial uplift, lowlands in Canada, Norway, Sweden and Russia are prone to formation of highly unstable, sensitive, and leached marine clay (quick clay). Quick‐clay failures are dramatic due to its high water content, resulting in liquefaction. It thus poses a major hazard for society and construction projects in particular, and knowledge of its extent is of vital importance. Quick‐clay assessment is usually undertaken in geotechnical boreholes having the disadvantage of giving only information at the borehole location. To overcome this limitation, geophysical ground‐based methods like electrical resistivity tomography have been used successfully. However, when a larger area has to be investigated, electrical resistivity tomography surveys become costly and time consuming. We show results from an airborne electromagnetic survey aiming at detection of different clay units for a road project in southeastern Norway. Airborne electromagnetic data clearly show structures within the sediment layer that correspond well with results from geotechnical boreholes. While a clear distinction between clay and quick clay cannot be derived from airborne electromagnetic alone, our study shows that this method has high‐enough resolution and accuracy to map differences in clay units, which can subsequently be probed at specified locations. Thus, by using airborne electromagnetics to target borehole locations, the costs for the geotechnical drilling program can be reduced significantly.
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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 | 0.000 | 0.001 |
| 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 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".