Mobility of dry granular flows of varying collisional activity quantified by smart rock sensors
Bibliographic record
Abstract
Highly instrumented particles (i.e., “smart rocks”) were included in monodisperse dry granular landslide experiments to quantify the collisional nature of such flows and to investigate the influence of collisional flow on the mobility of landslides. The total number of particles comprising a constant source volume of 0.4 m3 was varied by filling the volume with monodisperse particles of nominal diameters of 3, 6, 13 or 25 mm. Successively raising the total particle count resulted in flows that were increasingly thick relative to the respective particle size. Raw resultant acceleration data from the embedded smart rock sensors indicate that for each increase in grain size, there were increases in both the magnitude and frequency of particle collisions. Light detection and ranging (LiDAR)-generated point clouds of the landslide deposits indicated that increases in mobility and spreading, compared using differences in travel angle, were directly proportional to increases in collisional activity. By changing the size of the landslide particles from 3 to 25 mm, the travel angle at the gravity centre (αg) was observed to decrease from 27.8° to 25.3° (Δαg = −9.0%) and the Fahrböschung angle (α) was observed to decrease from 25.0° to 21.4° (Δα = −14.4%).
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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 | 0.000 |
| 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 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".