Generating a Statistically Constrained Quaternary Model of a Buried Bedrock Valley Using FDEM
Bibliographic record
Abstract
An airborne electromagnetic (AEM) survey was conducted using the Resolve™ frequency-domain system over a buried bedrock valley near Elora, Ontario, Canada. A statistical bootstrapping approach was used to establish a relationship between the electrical resistivity from spatially interpolated one-dimensional AEM resistivity models and the lithostratigraphy of Quaternary sediments logged in continuously cored holes located within and adjacent to the buried bedrock valley. Three lithology types were classified using a bootstrapping approach: (i) clay, (ii) sandy to muddy diamicton with the presence of clasts, and (iii) sand/gravel. The statistically derived ranges in electrical resistivity from the model were used to generate a lithostratigraphic model of the Quaternary deposits along the valley axis. The resulting lithology model differentiated more electrically resistive coarse-grained sand and gravel from electrically conductive finer-grained clay-rich tills; but was not able to resolve interbedded layers associated with complex fluvial deposits. Modeled Quaternary deposit architecture and bedrock morphology along two transects orthogonal to the valley axis were consistent with co-located surface electrical resistivity tomography models and borehole natural gamma logs, indicating that the AEM method, when calibrated using high-quality continuous-core logs, can support quantitative conceptualizations of complex Quaternary architecture within and around a buried bedrock valley. Key limitations in this approach were the reduced vertical resolution of the AEM method and the inability to resolve thinly bedded layers (meter scale) identified in the core logs that may have a hydrogeologic influence. This study demonstrates the utility of combining airborne electrical methods with high-resolution geological logs through statistical analysis to constrain hydrostratigraphic architecture at scales relevant to municipal groundwater flow systems.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".