Extraction and Comparison of Spatial Statistics For Geometric Parameters of Sedimentary Layers from Static and Mobile Terrestrial Laser Scanning Data
Bibliographic record
Abstract
ABSTRACT Terrestrial laser scanning (TLS) is a surveying technology that has seen increasing use in the field of geosciences in recent years. One potential application for this technology is to aid in quantitative stratigraphy. Given a point cloud containing multiple lithologies, the points associated with a specific lithology can be analyzed to quantify the geometric characteristics of that lithology, such as apparent dip, thickness, and spacing. In this study, a semi-automated work flow to perform such a characterization is presented and applied to a case study from an oil sands pit mine in the Athabasca region of Alberta, Canada. The results obtained using data collected with mobile and static TLS systems are compared to evaluate the effects of the various measurements and resolutions on the resulting stratigraphic statistics. In addition, mobile data collected for a small portion of the pit that was actively being mined are compared over time to evaluate changes in sedimentary layering in the direction perpendicular to the pit face. This component of the study highlights the impact of data quality on the resulting interpretations and represents a potential methodology for enhancing three-dimensional quantitative spatial modeling in a sedimentary environment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".