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Record W1484539808

An environmental noise impact assessment and forecasting tool for military training activities

2006· article· en· W1484539808 on OpenAlexvenueno aff
Roberto Racca, David Hannay, Scott Carr

Bibliographic record

VenueCanadian acoustics · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsnot available
Fundersnot available
KeywordsSoftwareSound propagationTerrainEnvironmental noiseAcousticsComputer scienceNoise (video)Interpolation (computer graphics)Impulse noiseRay tracing (physics)Impulse (physics)Sound (geography)Artificial intelligenceGeographyOpticsPhysics
DOInot available

Abstract

fetched live from OpenAlex

The features and applications of Impulse Noise Propagation Model (INPM) are discussed. The INPM is a noise forecasting tool. The software uses numerical estimation of the acoustic output for sources and interpolation of stored spectral source levels from a database to generate spectral source signals. The sound propagation module of software uses Parabolic Equation (PE) model that analyzes diffraction, air turbulence and sound interaction with the terrain. Ray tracing algorithm of the software is automatically invoked in place of PE for higher frequency. The INPM acoustic propagation module has the capability to output the complete sound level field in range and height along a radial from the source. The INPM has capability to lower noise impact on the environment by enabling informed planning and decision making.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.031
GPT teacher head0.264
Teacher spread0.233 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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