Rapid automated processing of structural orientation from time-of-flight LiDAR mapping
Bibliographic record
Abstract
The three-dimensional axis mapping (3DAM) method allows rapid capture and orientation of point cloud data to magnetic north in GPS and survey denied environments. From the collected point cloud data, the 3DAM algorithm automates the identification of rock structures and the processing of their orientation into a stereonet format, at the point of data collection, on a timescale measured in seconds. A tool, the Axis Mapper, was created based upon this method and is intended to streamline geological and geotechnical underground mapping of structural orientation data. The objective of this paper is to compare the precision and speed of capture for the Axis Mapper’s orientation measurement to the most prevalent method of orientation capture in underground mines, the structural compass. Structural orientation data captured from two case studies—one, at a base metals mine in South America; the second, two mines in Ontario, Canada—were compiled and compared for precision and speed between the Axis Mapper’s 3DAM method and a compass. Additional locations of data captured were planned but could not be collected for this paper. Sources of measurement interference, which could not be mitigated, were present at both case study locations. The data collected from the case studies suggest that structural orientation captured by the Axis Mapper and compass are within 11° dip direction and 5° dip of each other; except in one discreet example which identified a dip difference of 23°. The Canadian case study suggests the Axis Mapper is up to three times faster at collecting structural orientation data when compared with the compass. The authors consider the conclusions preliminary and non-definitive due to the limited number of collection sites, limited available data, and uncontrolled sources of measurement interference. Additional data collected in controlled settings are necessary to better define the comparative precision and collection speed of the Axis Mapper data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".