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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".