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Record W1534807495

Infrasound noise radiated from vibrating screens atan ore refinery: Part 2 - Noise reduction treatments and noise mapping technique

2011· article· en· W1534807495 on OpenAlexaffvenue
Louis-Alexis Boudreault, Michel Pearson, André L’Espérance

Bibliographic record

VenueCanadian acoustics · 2011
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsSoft dB (Canada)
Fundersnot available
KeywordsAcousticsNoise (video)InfrasoundSound pressureRangingSound intensityNoise reductionRefineryDynamic rangeScale (ratio)EngineeringPhysicsSound (geography)Computer scienceElectronic engineeringTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

An infrasound problem has been identified at an ore refining factory and large vibrating screens were identified as the dominant source. In order to evaluate potential solutions allowing to reduce the screens' acoustical emission, a 1:15 scale model was built to be tested in laboratory. Noise mapping techniques were used to highlight the radiating patterns and measure the performance of tested solutions. The displayed value is the sound intensity global level ranging from 205 to 235 Hz which represents the 13.5 to 15.5 Hz operating range for the full scale vibrating screen and the color scale ranges from 65 to 85 dB. The noise maps were performed at a 15 cm distance from the sound source. The lack of acoustical short-circuit generates a high pressure level in the hopper and the leak at the hopper's perimeter radiates the energy to the outside.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.023
GPT teacher head0.194
Teacher spread0.171 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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