Enhanced mud retention as an autogenic mechanism for sustained delta growth: Insight from records of the Lafourche subdelta of the Mississippi River
Bibliographic record
Abstract
ABSTRACT Mud deposition is acknowledged as a significant contributor to delta architecture, yet its role is often oversimplified as a constant parameter in models of delta formation. A better understanding of mud retention on deltas would resolve remaining questions regarding delta growth. This study explores how spatiotemporally varied mud retention facilitates sustained delta growth in defiance of the concept of autoretreat, that is, the idea that shoreline progradation rates decline as a delta grows due to the expansion of subaqueous and subaerial delta surfaces. This research is inspired by prior field observations of the river‐dominated Mississippi Delta, USA, where the shoreline of a ca 6000 to 8000 km 2 subdelta prograded at a constant rate for roughly a millennium, despite its expanding delta surface, compaction and sea‐level rise. For this, a laterally averaged one‐dimensional numerical model is leveraged to test hypotheses that enhanced mud retention with time in: (i) the delta bottomset; and (ii) the delta plain (floodplain) supports a constant rate of shoreline progradation in a maturing delta. Results demonstrate that enhanced mud retention in both the bottomset and delta plain facilitates sustained delta growth. Neither component by itself can replicate the case study. Yet, with these two integrated components, the model reproduces the cross‐section and linearly prograding pattern observed in the Mississippi Delta. The findings provide an autogenic mechanism for sustained delta growth and support the importance of mud as a fundamental building block of deltas that should be incorporated in delta‐growth models of engineered river diversions.
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) | 0.000 | 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 | 0.000 |
| 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 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".