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Record W4365455283 · doi:10.3390/proceedings2023086020

Evaluation of Vibration Emission Values of Nailers: Can an Automatic Test Stand Be Used Instead of Human Operators?

2023· article· en· W4365455283 on OpenAlexaff
Maxime Vincent, Thomas Padois, Marc-André Gaudreau, Thomas Dupont, Pierre Marcotte

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsUniversité du Québec à Trois-RivièresInstitut de recherche Robert-Sauvé en santé et en sécurité du travailÉcole de Technologie Supérieure
Fundersnot available
KeywordsVibrationNoise (video)Computer scienceCharacterization (materials science)Dispersion (optics)Test (biology)Artificial intelligenceSimulationReliability engineeringStructural engineeringAcousticsEngineeringPhysicsOptics

Abstract

fetched live from OpenAlex

To protect workers, it is necessary to characterize the noise and vibration emissions value of nailers. Standardized characterization methods exist but require three trained human operators, which leads to a dispersion in the results and a difficult implementation. An automatic test stand (ATS) was developed to characterize those values without the participation of human operators. It is proposed here that we compare the results obtained by the two methods. Preliminary results suggest that further refinement of the ATS is needed to better mimic the biodynamics of the human hand–arm and that a larger number of operators would be required for validation.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.057
GPT teacher head0.317
Teacher spread0.260 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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