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Record W2552439341 · doi:10.1121/1.4970012

Multipath propagation of sound in a shallow tidal channel and its implications on tomographic current measurements

2016· article· en· W2552439341 on OpenAlexaffabout
Mahdi Razaz, Len Zedel, Alex E. Hay, Kiyosi Kawanisi

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDalhousie UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsMultipath propagationAcousticsGeologyChannel (broadcasting)TomographySound propagationBroadbandSpeed of soundCurrent (fluid)Sound (geography)TelecommunicationsPhysicsOceanographyComputer scienceOptics

Abstract

fetched live from OpenAlex

A pilot observational experiment with Fluvial Acoustic Tomography (FAT) system was conducted in the Grand Passage, Nova Scotia, Canada, in 2014, to assess the capabilities of FAT in continuous monitoring of transport in a tidal channel. To implement the tomographic measurements, two broadband FAT transceivers operated at 7 kHz central frequency were positioned in both sides of the channel emitting a pulse every 30 s for 4 days. Three coherent arrivals were identified in the acoustic receptions. This paper investigates the influence of physical characteristics of water and current shear on multipath propagation of sound in well-mixed conditions of the channel. At the end, we report the comparison between FAT and reference velocity data collected by a moving-boat ADCP.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.060
GPT teacher head0.290
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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
Published2016
Admission routes2
Has abstractyes

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