Erosional control of the kinematics and geometry of fold‐and‐thrust belts imaged in a physical and numerical sandbox
Bibliographic record
Abstract
We investigate the effect of systematically varying erosion intensity ( K ) on the geometry and kinematics of fold‐and‐thrust belts modeled using equivalent conditions in physical and numerical experiments. Similar material properties and boundary conditions were used to compare numerical experiments to those modeled in the sandbox using an erosion rule that removes mass from the scaled sand wedges according to rates expected from bedrock fluvial incision. The geometry of both modeling approaches is quantitatively compared with that expected from analytical theory. The fold‐and‐thrust belt growth rate is inversely proportional to K and is well predicted by theory, except for the high erosion case, in which both the numerical and the experimental sandbox grows supercritically. A direct relationship exists between the erosion intensity and the number of fore‐shear bands and shear strain magnitude. The number of fore‐shear bands decreases, and their strain increases with erosion intensity. The results indicate that realistic and mechanistic erosion rules can be applied to physical experiments to model mass removal, and this approach opens up the possibility of calibrating strain history to erosion intensities for predictions in natural settings. Quantitative comparisons between the physical and numerical sandbox experiments indicate that deformation style and geometry of the thrust‐and‐fold belts are similar when identical rheologies and boundary conditions are used. This benchmarking suggests that numerical experiments realistically model conditions observed in the simple, cohesionless rheologies commonly employed in sandbox experiments. This suggests that numerical simulation may reliably model experimental deformation in geological situations that are difficult to represent in scaled experiments because of the different scales and rheologies of natural orogenic wedges.
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 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".