Autogenic vs Subsidence Controls on Fluvial Stratigraphic Grain Size Fining through Multi-Channel Landscape Evolution Modelling
Bibliographic record
Abstract
Grain size within the stratigraphic record is often used to interpret changes in tectonics and climate. For example, past work has described the influence of underlying subsidence or flux oscillations due to climate on grain size fining rates within the basin. However, little research has deconstructed the role of internal dynamics in shaping the grain size fining rates preserved within strata under varied basin geometries, precipitation gradients, and bypass states of basin evolution. Through the combination of a landscape evolution model based on the Stream Power Law modified for sedimentation by Yuan et al. (2019) with an extension of the self-similar grain size model of Fedele and Paola (2007) into multiple dimensions (i.e., along dynamically evolving river channels) by Wild et al (in review), we have developed a steady-state framework identifying autogenic vs subsidence dominated grain size fining. When basin accommodation is high relative to incoming flux, or early in the basin evolution, grain size fining is primarily subsidence-dominated regardless of precipitation gradients and basin geometries. Alternatively, under high bypass and low underlying accommodation, grain size fining is autogenically dominated and controlled by relative upstream discharge (or the ratio of the upstream, mountain catchment area vs the downstream, sedimentary system area). Foreland basins (eg: the Alberta foreland basin) with ample downstream area tend to evolve from a high subsidence to autogenic dominated state as they fill over time. Constrained downstream areas (eg: Death Valley fans) display a minimal autogenic impact on grain size fining regardless of their bypass state. We will present our modelled stratigraphic results and compare them to natural systems, such as the Alberta Foreland basin and alluvial fans of Death Valley.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".