Identification of Physical Transportation Infrastructure Vulnerable to Sea Level Rise
Bibliographic record
Abstract
The objective of this research was developing a methodology for assessing the potential impacts of sea level rise (SLR) on Florida’s state transportation infrastructure to assist the state with transportation planning. The proposed approach integrates the Florida Department of Transportation (FDOT) information system, satellite imagery, local roadway and hydrologic data with existing topographical and geographical data to generate SLR projections to facilitate i) the evaluation of current and projected SLR impacts on transportation infrastructure located along Florida’s coastline and low-lying terrain areas, and ii) the identification of the physical transportation infrastructure components that are vulnerable given the United States Army Corps of Engineers’ scenario-based methodology to project the timing of future low, intermediate and high rates of sea level change. A detailed case study in Dania Beach, Florida and a comparative example in Punta Gorda, Florida were used to evaluate the soundness of the methodology. Further research was performed to develop a preliminary evaluation of the impact of groundwater levels as an exacerbating factor with respect to sea level rise. Storm surge with SLR is a future, more difficult area of investigation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".