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Record W1986459416 · doi:10.1121/1.4708090

Assessment of geoacoustic inversion methods

2012· article· en· W1986459416 on OpenAlexaff
Ross Chapman

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsInversion (geology)Ground truthBenchmarkingGeologyWaves and shallow waterConsistency (knowledge bases)AcousticsTransmission lossComputer scienceSeismologyOceanographyMachine learningTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

Sound transmission in shallow water is strongly affected by the physical and acoustic properties of the ocean bottom. Over the past decade, sophisticated methods have been developed for estimating parameters of geoacoustic models that account for the interaction of sound with the bottom. The performance of the methods has been compared in benchmarking exercises for range-independent and range-dependent shallow water environments using simulated data. This paper extends the comparison of geoacoustic inversion methods to assess performance using data from experiments at sites where the ocean bottom environment was well known from independent ground truth information. There are several aspects to performance assessment. The comparison presented here shows the accuracy of estimates from various inversion methods compared to the ground truth data about the ocean bottom sediments. The methods that are compared include matched field inversion; perturbation techniques based on modal waveumber estimation; bottom loss measurements; travel time tomography; and in situ physical measurement. The assessment shows overall consistency from all the methods.

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)0.003
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: Methods · Consensus signal: none
Teacher disagreement score0.707
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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)0.0010.000

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 head0.035
GPT teacher head0.340
Teacher spread0.305 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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