Application of Coastal Resilience Metrics at Panama City Beach, Florida
Bibliographic record
Abstract
Coastal resilience is the ability of a system to prepare, resist, recover, and adapt to achieve functional performance under adverse events such as storms and sea-level changes. Usually, previously developed metrics are applied to quantify the resistance portion of coastal resilience, which supports the preparation and adaptation portions. Recent research produced a GIS-based tool that provides a quantitative coastal resilience metric to inform decisions related to protection from coastal storm impacts and to evaluate the success of past management actions. Other research developed a forward-looking metric that uses Beach-fx results to calculate future resilience based on nourishment alternatives. This study, for the first time, combines the Coastal Engineering Resilience Index (CERI) and Buffer Width (BW) metrics to better understand the historic, current, and future resilience of the coastal system at Panama City Beach, Florida. After the construction of the US Army Corps of Engineers Coastal Storm Risk Management (CSRM) project at Panama City Beach, the CERI resilience metric has increased up to 21.3%, while negative storm impacts in the same have been less than 8%. The frequency of nourishment efforts moving forward is justified by a 24.3% increase in the BW metric when comparing cases that are nourished frequently with cases that are not nourished frequently. Moreover, there is a 129.2% increase in the BW metric when comparing the frequently nourished cases with the cases that are nourished only on an emergency basis. While the CERI and BW metrics have both been considered previously, their combined application provides an understanding of a broader temporal view of how storm events, CSRM projects, and nourishments have played a part in the resilience of the system at Panama City Beach over the last two decades and how they may play a role in the next half century.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".