Recovering Sandy: Rehabilitation of Wastewater Pumping Stations after Superstorm Sandy
Bibliographic record
Abstract
On October 29, 2012, Superstorm Sandy brought devastation to the east coast. Storm surge and flooding affected a large swath of the State of New Jersey. Along with the homes and businesses, critical public infrastructure including water and wastewater treatment facilities was inundated. One such entity that was impacted was the Kearny Municipal Utilities Authority (KMUA). Two of KMUA’s pump stations were flooded on October 29th and on October 30th their road to recovery started. Beginning with initial operational restoration efforts, their journey has included but is not limited to design approaches to increase resiliency at these facilities and obtaining funding from various sources for construction. Recovery efforts are funded by FEMA the optimum hazard mitigation solutions for the KMUA’s facilities were investigated. Effective communication between the owner, its operators, engineers, regulatory, and funding agencies is paramount to successful completion of the recovery efforts in a timely manner. Resiliency design options to fit both the needs of the facility and work with in the operational goals of the organization were evaluated with the owner.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".