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Record W4235855347 · doi:10.31235/osf.io/yc2h8

Noise and the City: Leveraging crowdsourced big data to examine the spatio-temporal relationship between urban development and noise annoyance

2018· preprint· en· W4235855347 on OpenAlexaboutno aff
Andy Hong, Byoungjun Kim, Michael Widener

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsAnnoyanceNoise (video)Public healthEnvironmental healthPsychologyMedicineComputer scienceAudiology

Abstract

fetched live from OpenAlex

Noise is one of the most frequently complained nuisances and public health hazards. While traffic-related noise has been studied extensively, research on construction noise has been lacking. In this study, we examined the relationship between construction activities and noise annoyance and tested whether this relationship is stronger during after-hours. Data were drawn from a historical inventory of major development projects and crowdsourced citizen complaints data (311 calls) in Vancouver, Canada from 2011 to 2016. Mixed effects models were developed with an interaction between construction activities and after-hours report. Results show that neighborhood noise complaints were significantly associated with major constructions (IRR = 1.062, 95% CI = 1.024–1.097). A significant interaction effect was also found between construction activities and after-hours reporting (IRR = 1.050 CI = 1.012–1.087). To our knowledge, this is one of the first studies to empirically show adverse effects of urban development on noise annoyance. Results imply that existing noise bylaws may not be effective in restricting construction activities at night and during sleeping hours that may cause adverse health effects.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.009
Research integrity0.0000.001
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 head0.288
GPT teacher head0.396
Teacher spread0.108 · 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 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

Citations10
Published2018
Admission routes1
Has abstractyes

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