Autogenic vs Subsidence Controls on Grain Size Fining through Multi-Channel Landscape Evolution Modelling
Bibliographic record
Abstract
Abstract. Within the stratigraphic record, changes in grain size are often interpreted as a signature of external forcing events. However, it is not yet well constrained how autogenic processes (such as channel mobility) influence grain size signatures within the fluvial system. Here, we combine a landscape evolution model based on the Stream Power Law but modified for sedimentation (Yuan et al., 2019) with an extension of the self-similar grain size model Fedele and Paola (2007) to multiple dimensions (i.e., along dynamically evolving river channels) to study the relative importance of autogenic processes in con- trolling grain size fining. We first show how our new model can reproduce the results obtained by classical analytical solutions assuming that fining is controlled by subsidence only, in a single or amalgamated channel. We then show that deviations from past (subsidence and single channel only) predictions arise when varying two main parameters: first the ratio between the incoming sediment flux and integrated subsidence rate (F ), which increases with the degree of bypass of the system; and second, the ratio of the discharge leaving the mountain to the discharge generated within the subsiding basin (β), which controls the shape of the topography of the basin. We demonstrate that there exists two regimes, one corresponding to low values of F or high values of β, where the grain size fining is controlled by subsidence, and one corresponding to high F and low β values, where grain size fining is controlled by autogenic processes under steep topographic slopes that propagate sedimentary waves through the basin. Coupling the LEM to a flexural model predicts that grain size fining evolves from subsidence to autogeniccontrol in basins characterized by a progressive increase of F (under-filled to over-filled foreland), as seen in the case example of the Alberta Foreland Basin. Our results indicate that grain size fining during low filling conditions (e.g. early stage as the basin is forming) can indicate the dominantly tectonic controlled parameter of the flux relative to underlying subsidence ratio (F ); whereas, any fining under high bypass conditions (e.g. late stage once the basin is overfilled) can indicate the climate controlled upstream vs downstream ratio (β).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".