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Record W2159236189

A low cost wireless acquisition system for multi-channel vibration measurement

2011· article· en· W2159236189 on OpenAlexaffvenue
Pierre Marcotte, Sylvain Ouellette, Jérôme Boutin, Gilles LeBlanc

Bibliographic record

VenueCanadian acoustics · 2011
Typearticle
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travail
Fundersnot available
KeywordsData acquisitionUSBEngineeringAccelerometerVibrationWirelessElectrical engineeringRobustness (evolution)Electronic engineeringComputer scienceAcousticsTelecommunicationsPhysics
DOInot available

Abstract

fetched live from OpenAlex

A low cost wireless acquisition system for multi-channel vibration measurement is presented. An acquisition system based on National instrument NI-9234 boards and LabVIEW™ has been developed and successfully tested in underground mines for human vibration assessment. The acquisition system was built using two National Instrument NI-9234 USB boards, giving a total of 8 channels, an external LI-ION battery, one minicomputer with solid-state hard disk for added robustness, and a small waterproof Pelican case. The IEPE mode of the NI-9234 boards was used to supply electrical power to the accelerometers, requiring the use of AC coupling. Since the NE-9234 board has, in AC coupling, a roll-off of 3 dB at 0.5 Hz, a digital FIR filter was added to correct the low frequency response in whole-body vibration (WBV) measurement mode, in order to satisfy the newer standard ISO 8041 (2005).

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.009

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 head0.085
GPT teacher head0.231
Teacher spread0.146 · 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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