Near-Continuous Monitoring of a Coastal Salt Marsh Margin: Implications to Predicting Marsh Edge Erosion
Bibliographic record
Abstract
Mechanisms that control marsh edge erosion include wind-generated waves, vegetation, mudflats, anthropogenic factors, and geotechnical properties of sediments. However, existing models for predicting marsh edge evolution focus primarily on edge retreat rates as a function of wave energy while accounting for other controlling factors as empirical constants. This simplification rises from a lack of high frequency monitoring of marsh evolutions. In particular, marsh erosion is time-scale dependent and conducting field observations on short time and spatial scales could elucidate the progression of erosion, which may improve marsh erosion predictive models. This study developed and validated a near continuous camera monitoring system to document marsh edge erosion at a high frequency in Terrebonne Bay, Louisiana. Erosion pins were monitored with the cameras and daily erosion rates were estimated. This was supplemented with daily wave power to explore the relationships between daily erosion and wave power. The largest magnitude erosion events are driven by a buildup in wave energy over a seven-day time period coupled with a strong one-day wave event, indicating a gradual reduction in marsh edge resistance with continued wave attack. Long-term erosion monitoring methods, including monthly field visits, smooths over the large magnitude short-term erosion events. For example, satellite and aerial imagery provide a long period of record, but they seem to underestimate the average annual erosion rate in the region, the effect of which may become exasperated over the varying temporal scales considered in the planning efforts of projects meant to protect the Louisiana coastline.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".