MétaCan
Menu
Back to cohort
Record W2086205518 · doi:10.1121/1.2942982

A comparison of ray, normal-mode, and energy flux results for reverberation in a Pekeris waveguide

2007· article· en· W2086205518 on OpenAlexaff
Dale D. Ellis, Michael A. Ainslie, C. H. Harrison

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsReverberationBenchmark (surveying)WaveguideFlux (metallurgy)Mode (computer interface)Energy fluxAcousticsEnergy (signal processing)ScatteringNormal modeWork (physics)PhysicsComputational physicsComputer scienceOpticsMechanicsMaterials scienceGeology

Abstract

fetched live from OpenAlex

A number of problems were developed for, and presented at, a 2006 Reverberation Modeling Workshop sponsored by the US Office of Naval Research. The simplest of these known to the participants as Problem 11, was a Pekeris waveguide (isospeed water over a flat bottom half-space) with Lambert bottom scattering. The water depth was 100 m and frequencies of 250, 1,000, and 3,500 Hz were specified. A number of source-receiver combinations were specified, but the reverberation predictions are quite insensitive to sensor depth except at 250 Hz. With some benefit from hindsight and the results from other models, we compare our results from ray, normal-mode, and energy-flux approaches. All three approaches agree at intermediate times, say 3 to 50 s. At short times, the steep-angle paths and fathometer returns cause the mode and energy-flux models to underpredict the reverberation. At longer times, the ray models run out of steam: i.e., there are too many contributing ray paths for them to handle so they underpredict the reverberation. By combining the model predictions together with analytical results from an energy-flux model, we propose a composite benchmark solution. [Work supported in part 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: Methods · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score0.194

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 studies0.0000.000
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 head0.027
GPT teacher head0.309
Teacher spread0.282 · 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
Published2007
Admission routes1
Has abstractyes

Explore more

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