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Record W3197869332 · doi:10.1109/coa50123.2021.9520067

Moving Acoustic Source Transmission Trial in the Marginal Ice Zone of the Arctic

2021· article· en· W3197869332 on OpenAlexaboutno aff
Xueli Sheng, Chaoping Dong, Longxiang Guo

Bibliographic record

Venue2021 OES China Ocean Acoustics (COA) · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsAcousticsGeologyHydrophoneTransmission lossImpulse responseArcticReflection (computer programming)Impulse (physics)Interference (communication)Transmission (telecommunications)GeodesyTelecommunicationsComputer sciencePhysicsChannel (broadcasting)OceanographyMathematics

Abstract

fetched live from OpenAlex

A moving acoustic source transmission trial was conducted on the edge of the Canadian Basin during the Arctic Research Expedition. The sound source towed by the ship emits CW-LFM signals of 600-800Hz, and the signals are collected by an autonomous hydrophone. The matched filter result shows a clear multiple reflection structure, and as the distance increases, the time delay between multi-path gradually decreases. By comparing with the simulated channel impulse response, the source of the reflected signal is confirmed. The direct wave and reflections produce strong interference in the receiving end. In addition, there is an obvious convergence zone at a distance of 6 kilometers. Finally, this paper compares the transmission loss (TL) in the trial and the theoretical cylinder TL results. It turns out that the propagation loss in the trial roughly follows the cylinder expansion loss.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.238
Teacher spread0.221 · 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 designBench or experimental
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
Published2021
Admission routes1
Has abstractyes

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