MétaCan
Menu
Back to cohort
Record W1974963222 · doi:10.1121/1.4807317

Inversion for a moving spherical target's positional, structural, and speed parameters

2013· article· en· W1974963222 on OpenAlexaff
John A. Fawcett, Stan E. Dosso

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsEcho (communications protocol)ClutterAcousticsRADIUSWidebandSpeed of soundPhysicsPosition (finance)Doppler effectRange (aeronautics)OpticsBroadbandMultipath propagationComputational physicsComputer scienceRadarMaterials scienceChannel (broadcasting)Telecommunications

Abstract

fetched live from OpenAlex

The broadband scattering characteristics of a target may be used to distinguish its echoes from those of clutter. In a shallow water situation, the echo from the target will consist of a sequence of pulses corresponding to the various coherent combinations of incident and backscattered multipath arrivals. Thus, the interference effects of the waveguide propagation can have a significant effect upon the received echo and thus also affect the classification of the target from the echo. In addition, the target may be moving, in which case the received echo is also Doppler-shifted. The purpose of this paper is to investigate the simultaneous determination of a spherical target's position (range and depth) within a waveguide, its radial speed, its radius, its shell thickness, and the elastic parameters of the shell. It will be shown that many of these parameters can be accurately estimated from a single wideband echo, even in the presence of significant noise. The marginal probability distributions of the parameter values will also be investigated. It will be seen that the positional and speed parameters of the sphere can be determined to high precision. The other parameters are determined with varying degrees of precision.

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)0.000
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 score0.271
Threshold uncertainty score0.507

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 studies0.0000.001
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 head0.018
GPT teacher head0.246
Teacher spread0.228 · 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
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

Citations1
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicUnderwater Acoustics ResearchFrench-language works237,207