How reproducible is the acoustical characterization of porous media?
Bibliographic record
Abstract
There is a considerable number of research publications on the characterization of porous media that is carried out in accordance with ISO 10534-2 (International Standards Organization, Geneva, Switzerland, 2001) and/or ISO 9053 (International Standards Organization, Geneva, Switzerland, 1991). According to the Web of ScienceTM (last accessed 22 September 2016) there were 339 publications in the Journal of the Acoustical Society of America alone which deal with the acoustics of porous media. However, the reproducibility of these characterization procedures is not well understood. This paper deals with the reproducibility of some standard characterization procedures for acoustic porous materials. The paper is an extension of the work published by Horoshenkov, Khan, Bécot, Jaouen, Sgard, Renault, Amirouche, Pompoli, Prodi, Bonfiglio, Pispola, Asdrubali, Hübelt, Atalla, Amédin, Lauriks, and Boeckx [J. Acoust. Soc. Am. 122(1), 345–353 (2007)]. In this paper, independent laboratory measurements were performed on the same material specimens so that the naturally occurring inhomogeneity in materials was controlled. It also presented the reproducibility data for the characteristic impedance, complex wavenumber, and for some related pore structure properties. This work can be helpful to better understand the tolerances of these material characterization procedures so improvements can be developed to reduce experimental errors and improve the reproducibility between laboratories.
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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 | 0.055 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".