Augmenting geological field mapping with real-time, 3-D digital outcrop scanning and modeling
Bibliographic record
Abstract
Abstract Hand scanners are compact, lightweight, and capable of generating 3-D digital models. Although they do not compare to conventional methods (terrestrial laser scanning and photogrammetry) in terms of coverage, resolution, and accuracy, they offer increased mobility, speed, and real-time processing capabilities in the field. This study investigates the use of hand scanners for real-time, 3-D digital outcrop modeling to augment geological field mapping campaigns and highlights the advantages and the limitations. The utility of incorporating hand scanners as an additional tool for augmenting geological mapping is assessed based on 41 outcrop scans from the Gould Lake area, which is located 20 km north of Kingston, Ontario, Canada. The 3-D digital outcrop models gathered included two distinct metamorphic lithologies (marble and quartzofeldspathic gneiss) measuring up to 2.5 m high × 7 m long with an average surface area of 18 m2. This average scan size would take less than 10 min to capture, result in ~18 million individual points per scan, and provide a spatial resolution of ~1 cm for outcrop features. Throughout the course of the investigation, the main benefit of capturing multiple 3-D digital outcrop models was the ability to integrate this real-time, in situ geospatial, and geologic information across multiple visualization scales. This utility and retention of outcrop-scale geospatial information was shown to enhance the understanding of multi-scale geological relationships.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
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".