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Record W2037926121 · doi:10.1121/1.4809047

Benchmark workshop for geoacoustic inversion techniques in range dependent waveguides

2001· article· en· W2037926121 on OpenAlexaff
Ross Chapman, Stan Chin-Bing, David A. King, Richard B. Evans

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsInversion (geology)GeologyTransmission lossSynthetic dataAcousticsRange (aeronautics)Waves and shallow waterUnderwaterAlgorithmComputer scienceSeismologyOceanographyPhysicsMaterials science

Abstract

fetched live from OpenAlex

This paper summarizes the results from the ONR/SPAWAR Geoacoustic Inversion Techniques (IT) Workshop held in May 2001. The format of the workshop was a blind test to estimate unknown geoacoustic profiles by inversion of synthetic acoustic field data for vertical and horizontal array geometries in range dependent shallow water waveguides. The fields were calculated using COUPLE/RAM for three range-dependent test cases: a monotonic slope; a shelf break; and an intrusion in the sediment. Geoacoustic profiles were generated to simulate sand, silt and mud sediment environments. Different approaches for inverting the field data were presented at the workshop: model-based methods based on normal modes, parabolic equation or ray theory; perturbative methods; methods using transmission loss (TL) data; methods using vertical or horizontal array data. The geoacoustic profiles inverted by the different methods are compared using a metric based on calculation of transmission loss using the estimated profiles for geometries and source frequencies that were not used in the inversion. The results demonstrate the effectiveness of present day inversion techniques, and indicate the limits of their capabilities in range dependent environments. [Work supported by ONR.]

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)0.000
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: none
Teacher disagreement score0.892
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.025
GPT teacher head0.272
Teacher spread0.247 · 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

Citations2
Published2001
Admission routes1
Has abstractyes

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