A simplified geographical information systems (GIS)-based \nmethodology for modeling the topography of bedrock: illustration using the Canadian Shield
Bibliographic record
Abstract
Many geology, mining, and geotechnical applications require or depend upon some form of modeling of bedrock topography. Optimizing the manner with which bedrock topography is modeled poses a significant challenge because of the unpredictable or erratic presentation of the surface shape of bedrock. Unlike surface topography, bedrock topography is more difficult to determine because direct observation points are often not readily or directly accessible, unless the bedrock outcrops at the surface and is exposed, a relatively rare occurrence. When bedrock is covered by granular deposits, the only methods that allow practitioners to objectively establish the location of the top of the bedrock are to drill boreholes or conduct geophysical surveys. This makes the determination of bedrock topography not only difficult but also expensive. This study proposes a new approach for optimizing the modeling of complex bedrock topography, whose originality is based on the addition of “virtual” data points derived from cross-sections located between known boreholes. The proposed methodology is thus composed of four steps: gathering the maximum amount of relevant surface and subsurface data (from observation points), selecting the most appropriate technique for interpolating the observed bedrock elevations that will be entered into the dataset to be modeled, enriching the quantity of modeling data by adding “virtual” data elements based on geological interpretations of cross-sections (inserted into the model alongside the original objective data), and finally the modeling itself. The proposed approach is illustrated using data from a study area located in the Canadian Shield. Thousands of borehole records and surficial geological data as well as geological cross-section records were integrated to construct a three-dimensional bedrock topography model. The new proposed methodology can be applied to other regions worldwide.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".