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Record W2777200528

An Analysis of Two Cities and a State where Construction Noise and Vibration are Uniquely Regulated

2017· article· en· W2777200528 on OpenAlexvenueaboutno aff
Todd Busch

Bibliographic record

VenueCanadian acoustics · 2017
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Maintenance and Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsAnnoyanceNoise (video)EngineeringGovernment (linguistics)State (computer science)VibrationNoise controlCivil engineeringTransport engineeringInstrumentation (computer programming)Architectural engineeringComputer scienceAcousticsArtificial intelligenceNoise reduction
DOInot available

Abstract

fetched live from OpenAlex

In Canada and the United States, there are three places where construction noise and/or vibration are uniquely regulated by government authorities. These include the City of Toronto (Ontario), the City of New York, and the sunny State of California. The regulations in place require a combination of studies prior to construction activity and/or monitoring of noise and vibration during construction, using instrumentation that is specially designed for this purpose. The underlying objective of these regulations represents either a commitment to avoidance of damage to structures that are in proximity to a construction site, or the reduced probability of public annoyance due to on-going construction noise and vibration. An overview will be provided of the regulations in place for these three locations along with a number of case studies of particular projects where professional expertise has been applied in search of compliance with the requirements of regulations. The lessons learned from these case studies are presented.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.330
Threshold uncertainty score0.997

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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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 designObservational
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
Published2017
Admission routes2
Has abstractyes

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