A Framework for Back-Analysis of 3D Rockfall Trajectories
Bibliographic record
Abstract
We define a novel normalized loss function to quantitatively evaluate the goodness-of-fit between simulated and measured rockfall trajectories using elapsed time and sampled rock positions.This loss function is optimized to backanalyze the coefficients of restitution R n and R t using a Monte-Carlo search of the parameter set θ = [R n , R t , v 0 ] where v 0 is the initial horizontal velocity.The trajectories are simulated assuming lumped mass rocks with initially horizontal projectiles and zero rotation.While our results are derived using position as the loss term, we note that our framework is entirely compatible with velocity or energy as a loss term as suggested by other researchers.The efficacy of the backanalysis framework is examined using synthetic and measured rockfall trajectories from a copper mine in British Columbia, Canada.The Monte Carlo search reveals significant non-uniqueness in the back-analyzed values of R n and R t , which can be mitigated by joint back-analysis that stacks the loss contour of multiple target trajectories.Parametric studies suggest that a minimum of 10,000 Monte Carlo samples should be simulated for an accurate solution, and that the spatial resolution of the topography is linearly correlated to the minimum loss.This measured trajectory was also used to test the viability of scaling R n by velocity and mass.Our results suggest that velocity scaling performs similarly (12% deviation from measured path) to a static R n value (9% deviation) while the measured trajectory cannot be satisfactorily reproduced (43% deviation) when scaling R n by mass.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".