MétaCan
Menu
Back to cohort
Record W2997884749

Innovative and Feasible Noise Mitigation Planning

2019· article· en· W2997884749 on OpenAlexaff
Amir A. Iravani, Lucas Arnold

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsDillon Consulting
Fundersnot available
KeywordsChangeoverNoise (video)UpgradeTimelineNoise controlNoise pollutionEngineeringProcess (computing)Noise barrierPlan (archaeology)Transport engineeringRisk analysis (engineering)Computer scienceBusinessTelecommunicationsNoise reduction
DOInot available

Abstract

fetched live from OpenAlex

For industrial sites located near residential areas, noise pollution can be a limiting factor in expanding production. When KFP Inc. wanted to upgrade machinery and increase their annual throughput, they faced challenges dealing with increasing noise levels above acceptable limits at the neighbouring residential community. They needed a solution that would achieve continuous noise shielding, but a permanent noise barrier wall would cost millions of dollars and extend the project timeline. We envisioned a way to arrange for stacks of logs to be used as noise barriers and developed a plan to maintain continuous shielding even as the log inventory is removed for processing. The log wall changeover plan was developed through implementation of parallel walls system. By prototyping this barrier and completing the changeover process with sensors in place, we confirmed that our noise mitigation strategy was successful. This solution demonstrates the value and viability of using available onsite materials to provide noise mitigation and suggests the approach can be applied more widely. With good design, thorough investigation, and creative planning, regulatory compliance and improved noise environment for sensitive receptors in proximity of industrial facilities can be achieved.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.668

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)0.0000.001

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 head0.036
GPT teacher head0.410
Teacher spread0.374 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicNoise Effects and ManagementFrench-language works237,207