Identification of Physical Transportation Infrastructure Vulnerable to Sea Level Rise
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it