Applications of Forward Stratigraphic Modelling in Modern Siliciclastic Settings: A Case Study from the Fraser River Delta, Canada
Bibliographic record
Abstract
Forward stratigraphic modelling (FSM) is a relatively new approach that is used to test the importance of parameters that control stratigraphic stacking patterns and to reveal uncertainties such as sedimentation rate and accommodation space. Although FSM is commonly employed in the study of ancient systems, it is rarely applied to modern settings. The Fraser River Delta in Canada provides an opportunity to test applications of FSM in recently deposited sediments in an active sedimentary basin. Because it is un-dammed, the river enables comparison of the modern and ancient systems. It is also a well-studied river system, with sufficient data to generate a realistic model for predicting future scenarios. In this study, Dionisos software is used, and the evolution of the delta over the past 10,000 years is successfully simulated in two steps (5000 years each) using both realistic and real-time data. The main controlling parameters are observed to be the sediment supply and water discharge values, and to a lesser extent, sea level variation. Several possible future scenarios are tested, changing the main parameters to understand and to predict future morphological changes and stacking patterns. Increasing the main parameter values resulted in progradation, while reducing resulted in erosion, particularly in the subaqueous section of the delta. The results of this study can be used to calibrate numerical modelling applications in both modern and ancient deltaic settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".