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Record W2625872054 · doi:10.1121/1.4988881

Some practical issues affecting repeatability and reproducibility in laboratory transmission loss tests

2017· article· en· W2625872054 on OpenAlexaff
Christoph Hoeller, Jeffrey Mahn

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsRepeatabilityReproducibilityTransmission lossTransmission (telecommunications)Computer scienceMaterials scienceAcousticsReliability engineeringMathematicsStatisticsEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

The ASTM standard E90 defines the measurement of transmission loss, equivalent to the sound reduction index defined in ISO 10140. ASTM E90 and ISO 10140 specify requirements for the laboratory, the test procedure and conditions, and for preparation and mounting of the specimen under test. Despite the strict requirements in ISO 10140 and the somewhat less strict requirements in ASTM E90, transmission loss results for nominally identical specimens often vary if measured in different laboratories, and sometimes even if measured again in the same laboratory. In practice, there are many factors that affect the repeatability or reproducibility of a transmission loss test for a given specimen. This presentation will not attempt to systematically cover all different sources of uncertainty, but instead will highlight some practical issues commonly encountered in laboratory transmission loss tests. Examples will be presented for a number of issues, including the effect of leakage through the specimen under test, the effect of varying temperature and humidity in the test chambers, and the effect of re-using gypsum board.

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)0.006
metaresearch head score (Gemma)0.007
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 score0.679
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.326
Teacher spread0.302 · 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
Published2017
Admission routes1
Has abstractyes

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