Living Shoreline Treatment Suitability Analysis: A Study on Coastal Protection Opportunities for Sarasota County
Bibliographic record
Abstract
Increases in the world population, sea level rise, and urbanization of coastal areas have put tremendous pressures on coastlines around the world. As a result, natural shoreline habitats are being replaced by seawalls and other hardened forms of coastal protection. Evidence shows that hardened shorelines can have a negative impact on the environment and surrounding habitat, leading to a loss of biodiversity and ecosystem services. This research aims to increase the different forms of coastal protection used throughout Sarasota County, Florida by conducting a geographic information system (GIS) suitability analysis for living shoreline treatment. Living shorelines or hybrid solutions are a more ecologically sustainable alternative to traditional forms of coastal protection, which use natural ecosystems or alternatively- structural organic and natural materials such as plantings, rocks, and oyster beds to stabilize shorelines and enhance shoreline habitat. The GIS model identifies coastlines that are 1) most suitable for living shoreline treatment, 2) most suitable for a hybrid solution, or 3) not suitable for living shorelines by analyzing the bathymetry, land use, land value, tree canopy, population, wave energy, shoreline sensitivity, and shoreline habitat. The suitability for living shoreline treatments was assessed independently for each parameter and assigned a value ranging from 0, areas that should consider using traditional methods of coastal protection to 3, shoreline segments most suitable for living shoreline treatment. The results from the individual analyses for each parameter were combined using a weighted overlay approach to determine general suitability for living shorelines within the study area. The result found that over 95% of the shoreline segments are potentially suitable for hybrid shoreline stabilization solutions.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".