Grain size dynamics using a new planform model – Part 3: Stratigraphy and flexural foreland evolution
Bibliographic record
Abstract
Abstract. Within the stratigraphic record, grain size fining has been commonly used to infer subsidence, rate and its variability has been interpreted as a signature of external forcing events. We have recently developed a model (Wild et al., 2025b) that predicts grain size fining within a two-dimensional Landscape Evolution Model to predict the effect of autogenic processes on grain size fining. Here, we couple it to a flexural model to predict the stratigraphic evolution of a foreland basin, the distribution of grain size fining, and which of subsidence or autogenic processes dominates in controlling the fining. We show that, throughout its evolution, the foreland basin experiences a gradual increase in the bypass ratio, F, that provokes a gradual shift from subsidence-dominated to autogenically dominated grain size fining but also progressively alters stratigraphic preservation. The amplitude, and therefore efficiency, of autogenic processes in controlling grain size fining processes is modulated by the shape of the surface topography that we control by changing the rainfall gradient and extent of the basin confinement compared to the orogen. We also show how the evolution of the basin can be mapped in the framework we recently developed (Wild et al., 2025c) to interpret grain size fining data. Finally, we demonstrate how the model results and our findings can be used to interpret the stratigraphy and grain size information stored in a real foreland basin, namely the Alberta Basin of Western Canada.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".