Comparison of creek and bay influences on salt marsh sediment budget and deposition patterns
Bibliographic record
Abstract
Abstract The resilience of salt marshes with low organic production depends on their effective capture and retention of mineral sediment from adjacent waters. Little prior work has directly compared mechanisms of sediment import from wave‐influenced marsh boundaries against those of tidal creeks. We used simultaneous deployment of net‐deposition tiles and oceanographic sensors to identify the timing and magnitude of sediment import/export to, and redistribution within, a marsh in south San Francisco Bay. As the marsh has both an eroding bay‐exposed scarp and a prominent tidal creek, we investigated the mechanisms and magnitudes of sediment import from the marsh‐bay versus the marsh‐creek interface. The strong daily sea breezes of the summer season produced most of the wave‐driven erosion of the marsh scarp and controlled suspended sediment concentrations; the winter season had weaker winds punctuated by a few storms. A large seasonal difference in suspended sediment concentrations influenced both flood and ebb sediment fluxes to the marsh and led to much higher rates of import in the summer. Both bay‐side and creek‐side processes were important to total marsh sediment budget. Bay‐side sediment contributions were more variable in time due to the bay‐influenced environment, and creek‐side contributions were overall larger, reflecting the large proportion of the marsh fed by creek water. Sediment was redistributed throughout the system, with erosion near the bay‐edge, accretion near the creek‐edge and slow import to the marsh interior. The marsh was net importing sediment in the summer and exporting in the winter from different rates of these processes; on an annual scale, the marsh was net importing despite rapid lateral marsh loss. These findings emphasize that a positive sediment budget does not imply a stable marsh and that both creek‐ and edge‐side dynamics are important for marsh sedimentation and geomorphic trajectories. Further, we expand understandings of non‐storm and seasonal controls on marsh sedimentation. Copyright © 2025 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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) | 0.000 | 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".