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Record W1898527705 · doi:10.25043/19098642.94

Underwater Multi-influence Measurements as a Mean to Characterize the Overall Vessel Signature and Protect the Marine Environment

2014· article· en· W1898527705 on OpenAlexaff
Antonio Sánchez García, Adolfo Hernández Solano, F. Javier Rodrigo Saura, Patricio Muñoz Esparza

Bibliographic record

VenueCiencia y tecnología de buques · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsInternational Submarine Engineering (Canada)
Fundersnot available
KeywordsUnderwaterSignature (topology)Environmental scienceMarine engineeringModular designComputer scienceAcousticsEngineeringGeologyOceanographyPhysics

Abstract

fetched live from OpenAlex

The overall signature of a vessel comprises acoustic, magnetic, electric-field, pressure, and seismic radiations. Over the past years, the international community’s efforts have mainly centered on reducing the acoustic influence with the dual aim of decreasing the vessel’s detectability and reducing the levels of acoustic pollution generated in the marine environment. Nowadays, the need to act not only on the acoustic radiation but against the overall set of the vessel’s radiations is becoming increasingly clear, both in the military and the civilian fields, based on aspects like vessel stealthiness, security of harbor and critical infrastructures, and environmental protection. As a key element to achieve this goal, it is greatly important to have at our disposal highly modular and adaptable measurement systems covering the overall set of the vessel’s radiations, with a high capacity of data transmission to base centers to have the capacity to make measurements in all kinds of marine environments.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.030
GPT teacher head0.237
Teacher spread0.207 · 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 designObservational
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

Citations3
Published2014
Admission routes1
Has abstractyes

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