MétaCan
Menu
Back to cohort
Record W2951538072 · doi:10.1177/1351010x19855227

Calibration of the ISO tapping machine for finite-element prediction tool on a wooden-base floor

2019· article· en· W2951538072 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBuilding Acoustics · 2019
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsTappingCalibrationFinite element methodComputer scienceBase (topology)Noise (video)Range (aeronautics)Structural engineeringMechanical engineeringAcousticsEngineeringMathematicsAerospace engineering

Abstract

fetched live from OpenAlex

One important challenge of the wooden constructions is to achieve a high quality of acoustic insulation, especially decreasing the impact noise in the low-frequency range. In order to avoid over-designed solutions and expensive experimental tests in the design phase, reliable prediction tools are called for. This article is an initial investigation of modeling the ISO standardized tapping machine on a cross-laminated timber floor, using finite element method. The wooden-based floor was first calibrated in terms of its dynamic properties. The influence of the material properties of the cross-laminated timber floor was discussed. The force generated by the tapping machine was then introduced in the established cross-laminated timber model. The model was finally validated by comparing the simulation results with the measured accelerations.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.315

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)0.0000.000

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 head0.012
GPT teacher head0.203
Teacher spread0.191 · 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