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Record W2759499798 · doi:10.1121/1.5003788

Short-pulse method for acoustic backscatter amplitude calibration at MHz frequencies

2017· article· en· W2759499798 on OpenAlexafffund
G. Wilson, Alex E. Hay

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBackscatter (email)CalibrationAcousticsAmplitudeMaterials scienceAttenuationOpticsPulse (music)ScatteringRayleigh scatteringTransducerPhysicsDetectorTelecommunications

Abstract

fetched live from OpenAlex

The short-pulse acoustic backscatter amplitude calibration technique of Dragonette, Numrich, and Frank [(1981), J. Acoust. Soc. Am. 69, 1186-1189] is evaluated for MHz frequency systems, as are used to measure environmental suspended sediment concentration. The technique involves measuring backscatter from a short transmit pulse reflected by a solid spherical target, and treating the first received reflection as equivalent to that from a perfectly rigid sphere. In this implementation, the target sphere is 16 mm diameter tungsten carbide. The target is mounted on a stiff support rod rather than being suspended on a filament, enabling precise positioning of the sphere relative to the transducer. That the support rod does not significantly influence the calibration is demonstrated by reconstructing the backscatter time series, including the sequence of pulses from surface (Rayleigh) waves, using elastic scattering theory. Calibration results are verified in a separate experiment in which the backscatter from dilute aqueous suspensions of 400-600 μm diameter polystyrene beads in water is measured within the 1-2 MHz frequency band. After correcting for the additional attenuation within the scatterers, the resulting values of the backscatter form factor are within 20% of the theoretical predictions.

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)0.002
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.044
GPT teacher head0.318
Teacher spread0.274 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations5
Published2017
Admission routes2
Has abstractyes

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