Estimation of the accuracy of measured acoustical parameters of porous materials with an acoustical method
Bibliographic record
Abstract
Acoustical parameters such as tortuosity, viscous and thermal characteristic lengths, or even static thermal permeability are often required to describe the dynamic behavior of porous materials. Using a middle frequency method, based on the use of a standing waves tube, the direct measurements of the resistivity and porosity, and the accurate measurement of the dynamic density and bulk modulus, these intrinsic parameters can be obtained from analytical inversion of Johnsons and Lafarges models. The interests of the method lie in the simplicity of the apparatus and on the clear separation of viscous and thermal dissipative contributions. This last point specially helps to understand the physics of the medium and check the validity of the assumptions made: the material should not have a strong elastic behavior, and it must fit the models used in the inversion process. In this presentation, the robustness of the method is discussed and estimations of the measurement uncertainties are given. The occurrence of systematic errors coming from the use of semi-phenomenological models for determining intrinsic parameters is tackled. The measurement procedure, and the experimental set-up are detailed, and results obtained on materials with very different properties are presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".