Extraction and Comparison of Spatial Statistics For Geometric Parameters of Sedimentary Layers from Static and Mobile Terrestrial Laser Scanning Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it