Bibliographic record
Abstract
Rockfalls can often pose a significant risk to the public if protective designs do not properly account for block movement downslope and onto infrastructure facilities. Assessing these hazards is challenging, especially as current empirical and computational methods for predicting trajectories of falling blocks generally do not include the contribution associated with rotational behavior. Research undertaken at the University of New Hampshire, USA, over the last decade has led to the development of Smart Rock (SR) sensors inserted in natural rocks to evaluate these events from the perspective of the falling rock. The latest SRs consist of 3 D printed capsules 58.0 mm in length and 25.4 mm in diameter, equipped with a ±400 g and a ±16 g 3-axis accelerometer, a ± 4000 dps high-rate gyroscope, and an altimeter. Approximately 80 field experiments conducted in New Hampshire and Vermont provided SR data on rockfall at ten different sites. The Smart Rock data allowed more in-depth evaluations of accelerations, rotation rates, and modes of motion with precise time intervals, which cannot be easily captured in video recording systems or other instrumentation techniques. Such measurements are essential to improve our understanding and modeling of rockfall events for more economical and safer design of protective systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".