Grain size dynamics using a source-to-sink approach to planform modelling
Bibliographic record
Abstract
The grain size preserved within the stratigraphic record over thousands to millions of years has a wide breadth of relevance for deciphering past tectonic and climatic events within the continental sedimentary system and within applications for reservoir prediction, groundwater modelling and floodplain weathering. Here, we present a new model for grain size fining predictions that couples a landscape evolution model (with deposition and erosion components (Yuan et al. 2019)) with a self-similar model for gravel and sand (Fedele and Paola 2007). The new model, that we called GravelScape, includes the e"ects on grain size fining of lateral heterogeneity in deposition rate caused by dynamically evolving channels. Through an initial validation, we show that when channel avulsions are prevented, by reducing the planform model to a single, downstream dimension, our new model can reproduce results from past methods (e.g. (Duller et al. 2010)) that assume that fining is controlled by subsidence only. However, deviations in predicted grain size occur when the e"ects of a multi-channel (internal or autogenic dynamics) planform model are considered. The amplitude of these deviations seems to be proportional to the extent of sedimentary system bypass and the shape of the surface topography, which influence the magnitude of across-basin variation within the sedimentary system. Under low bypass and gentle slopes, grain size trends are primarily governed by subsidence, while high bypass and steep topography enhance autogenic influences. Simulating shorter transport lengths (e.g. decreasing orogen discharge, increasing basin area, or increasing transient sediment in the basin) tend to generate more autogenic related fining and variance within a basin. We demonstrate how these dynamics of grain size fining can be mapped using a framework that links by-pass and surface geometry, with practical applications for foreland basin evolution illustrated using data from the Alberta Basin in Western Canada. As the by-pass ratio increases over time, as seen in foreland basins, there is a transition from subsidence to autogenicall dominated grain size fining. We also test under which conditions Milankovitch scale precipitation perturbations are best captured within the continental alluvial fan system considering autogenic variability, perturbation strength, and preservation. We then apply a detailed misfit comparing the model to data in the Grapevine mountain fans of Death Valley, where both climate and autogenic fan responses have been observed. Our research contributes to the interpretation of grain size trends in natural systems and their response to both autogenic and external forces. Prior to this dissertation, no landscape evolution model could e!ciently (within minutes) predict grain sizes preserved within the sedimentary record over large spatial and long temporal scales in response to climatic, tectonic, and internal forcing.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".