A Case Study of Urban Coastal Resilience: The Battery
Bibliographic record
Abstract
Waterfront infrastructure must be both resilient and sustainable. The Battery Coastal Resiliency Project, situated at the southern tip of Manhattan, is part of a series of projects that comprise the Lower Manhattan Coastal Resilience (LMCR) program and includes both climate mitigation and climate adaptation practices. Life-cycle analyses considerably reduced the project’s embodied carbon, while the project design adapted to climate change impacts, such as sea level rise and storm surge. The project is reconstructing the aging and deteriorating wharf originally constructed in the 1940s with a new elevated wharf that provides upland park protection from the 90th percentile sea level rise scenario in the year 2100 as defined by the New York City Panel on Climate Change (NPCC). Elevating the wharf 5 ft required extensive coordination with the National Park Service (NPS) vessel operators who ferry approximately 4 million tourists per year to the Statue of Liberty from the Battery. The wharf design had to consider present-day operability while planning for future climate change conditions all while maintaining universal design principles. Those challenges led to an innovative two-slip system that allows vessels to berth at a slip that best suits the tidal elevations. The technical design of the wharf accounted for several challenges, including upland historic structures, such as Castle Clinton and the Pier A Building, and several monuments and artworks. Subsurface conditions further complicated the structural design considering poor geotechnical soils and existing infrastructure, such as several internal and combined sewer overflow outfalls, the Battery Underpass, the Hugh L. Carey Tunnel, and subway tunnels.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".