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Record W616051664

Doremus Avenue Bridge Replacement

2005· article· en· W616051664 on OpenAlexvenueno aff
Thomas W. Anella

Bibliographic record

VenueBridges Conversations in Global Politics and Public Policy · 2005
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsBridge (graph theory)EngineeringSpan (engineering)GirderCivil engineeringRelocationBuilding codeStructural engineeringConstruction engineeringForensic engineeringComputer science
DOInot available

Abstract

fetched live from OpenAlex

This article describes a New Jersey Department of Transportation (NJDOT) project to replace the 1918 Doremus Avenue Bridge in Newark. The project, which features load and resistance factor design (LRFD) and smart bridge technology, has become a prototype for future bridge construction for NJDOT. The deteriorated 1,275 ft bridge spans 33 railroad tracks. The project involved replacing 18 single-span through-girder units with a new 9-span bridge using three 3-span continuous units. An extensive study phase and preliminary design phase for the project lasted four years. Between the preliminary and final design phases, the NJDOT adopted the AASHTO LRFD code, which saved approximately $1.6 million in construction costs compared to the more conservative load factor design method. The Doremus Street Bridge also served as a test site for research on smart bridge technology. Instrumentation was installed in one 3-span unit of the bridge to assess the actual stresses induced into the structure. Real-time information will be collected and analyzed to compare and assess the actual stresses under routine dead and live loading conditions compared with the anticipated behavior of the bridge under the new LRFD code. Major project challenges, including the need to blend in with the historical railroad corridor, the relocation of utilities and environmental impact minimization, are also discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.758

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)0.0000.000

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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2005
Admission routes1
Has abstractyes

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