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Record W4412812629 · doi:10.3397/nc_2025_0046

Evaluation of alternative highway noise abatement strategies for one community

2025· article· en· W4412812629 on OpenAlexaff
Christopher W. Menge, Robert Finck, Scott Noel, Logan Katsoufis

Bibliographic record

VenueNOISE-CON proceedings · 2025
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsMiller Group (Canada)
Fundersnot available
KeywordsRoadway noiseEnvironmental scienceNoise (video)Environmental planningTransport engineeringTraffic noiseComputer scienceEngineeringNoise reductionArtificial intelligence

Abstract

fetched live from OpenAlex

For the Massachusetts Department of Transportation (MassDOT), HMMH undertook an investigation of the potential benefits of several noise abatement options for a community of single-family homes located along I-90. This investigation was prompted by noise complaints and was not associated with a roadway improvement (Type I) project or barrier retrofit (Type II) project. MassDOT scoped HMMH to evaluate many options that could potentially reduce existing traffic noise levels to below the applicable FHWA Noise Abatement Criteria (NAC) of 66 dBA, Leq in the loudest hour of the day. The strategies included a vegetative buffer, safety barrier, privacy fence, earth berm, and noise barrier along the highway and along the residential property line. The paper details the analysis approaches taken and results of each strategy. The variable terrain throughout the study area presented challenges to achieving the goal with any of the individual strategies, but a combination of the two noise barrier placements was predicted to be successful.

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)0.007
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.137
GPT teacher head0.448
Teacher spread0.311 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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