Impact of surface processes on the growth of orogenic wedges: Insights from analog models and case studies
Bibliographic record
Abstract
Interaction between surface processes and deep tectonic processes plays a key role in the structural evolution, kinematics and exhumation of rocks in orogenic wedges. The deformation patterns observed in analogue models applied to natural cases of present active or ancient mountain belts reflect several first order processes that result of these interactions. Internal strain partitioning due to mechanical behaviour of a thrust wedge has a strong impact on the vertical component of displacement of tectonic units that in return favour erosion in domains of important uplift. Such strain partitioning is first controlled by tectonic processes, but surface processes exert a strong feed back on wedge dynamics. Indeed, material transfer in thrust wedges not only depends on its internal dynamics, it is also influenced by climate controlled surface processes involving erosion and sedimentation. Effects of erosion are multiple: they allow long term localization of deformed domains, they favour important exhumation above areas of deep underplating and combined with sedimentation in the foreland they contribute to maintain the wedge in a critical state for long time periods. The simple models illustrate well how mountain belts structure, kinematics of tectonic units and exhumation are determined by these complex interactions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".