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Record W2408224679 · doi:10.1061/9780784479889.031

Recovering Sandy: Rehabilitation of Wastewater Pumping Stations after Superstorm Sandy

2016· article· en· W2408224679 on OpenAlexaff
Ceren L. Aralp, John J. Scheri, Kevin O’Sullivan

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2016 · 2016
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsFlooding (psychology)Work (physics)Storm surgePublic workEnvironmental planningHazardEnvironmental scienceWater infrastructureStormBusinessCivil engineeringEngineeringEnvironmental resource managementEnvironmental engineeringGeographyPolitical scienceWater supplyPublic administrationMeteorology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.003
GPT teacher head0.170
Teacher spread0.167 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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