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Record W1518560149

Infrasound noise radiated from vibrating screens at an ore refinery: Part 1 - Problem analysis and small scale model design

2011· article· en· W1518560149 on OpenAlexaffvenue
Louis-Alexis Boudreault, Michel Pearson, André L’Espérance

Bibliographic record

VenueCanadian acoustics · 2011
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsSoft dB (Canada)
Fundersnot available
KeywordsInfrasoundAcousticsVibrationNoise (video)Sound pressureInflowScale modelFactory (object-oriented programming)Scale (ratio)Measure (data warehouse)EngineeringGeologyComputer sciencePhysicsAerospace engineering
DOInot available

Abstract

fetched live from OpenAlex

An infrasound problem has been identified at an ore refining factory. Despite being inaudible, the infrasound levels are high enough to generate discomfort among workers and to generate vibration in the building structure. Noise mapping techniques were used to highlight the radiating patterns and measure the performance of tested solutions. The vibrating screen consists of a concave shaped vibrating plate. The ore inflow comes at the top of the screen and the coarse particles go out at the bottom. The screened particles are collected using a pyramidal shaped hopper. Acoustical short-circuits, or holes in the screen plate were made in order to equilibrate the acoustical pressure on each side of the screen plate. The holes also reduce the radiating area of the screen plate reducing even more the generated sound pressure.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.052
GPT teacher head0.208
Teacher spread0.156 · 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 designSimulation or modeling
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
Published2011
Admission routes2
Has abstractyes

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