High-resolution three-dimensional imaging and analysis of rock falls in Yosemite Valley, California
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Bibliographic record
Abstract
We present quantitative analyses of recent large rock falls in Yosemite Valley, California, using integrated high-resolution imaging techniques. Rock falls commonly occur from the glacially sculpted granitic walls of Yosemite Valley, modifying this iconic landscape but also posing significant potential hazards and risks. Two large rock falls occurred from the cliff beneath Glacier Point in eastern Yosemite Valley on 7 and 8 October 2008, causing minor injuries and damaging structures in a developed area. We used a combination of gigapixel photography, airborne laser scanning (ALS) data, and ground-based terrestrial laser scanning (TLS) data to characterize the rock-fall detachment surface and adjacent cliff area, quantify the rock-fall volume, evaluate the geologic structure that contributed to failure, and assess the likely failure mode. We merged the ALS and TLS data to resolve the complex, vertical to overhanging topography of the Glacier Point area in three dimensions, and integrated these data with gigapixel photographs to fully image the cliff face in high resolution. Three-dimensional analysis of repeat TLS data reveals that the cumulative failure consisted of a near-planar rock slab with a maximum length of 69.0 m, a mean thickness of 2.1 m, a detachment surface area of 2750 m2, and a volume of 5663 ± 36 m3. Failure occurred along a surface-parallel, vertically oriented sheeting joint in a clear example of granitic exfoliation. Stress concentration at crack tips likely propagated fractures through the partially attached slab, leading to failure. Our results demonstrate the utility of high-resolution imaging techniques for quantifying far-range (>1 km) rock falls occurring from the largely inaccessible, vertical rock faces of Yosemite Valley, and for providing highly accurate and precise data needed for rock-fall hazard assessment.
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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) | 0.004 | 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