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Record W2521034421 · doi:10.1121/2.0000246

Soundscape characterization in a dynamic acoustic environment: Grand Passage, Nova Scotia, a planned in-stream tidal energy site

2016· article· en· W2521034421 on OpenAlexaffabout
Anne R. Lombardi, Alex E. Hay, David R. Barclay

Bibliographic record

VenueProceedings of meetings on acoustics · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSoundscapeEnvironmental scienceSound (geography)AcousticsGeologyOceanography

Abstract

fetched live from OpenAlex

The marine environment in the Bay of Fundy hosts a dynamic and diverse soundscape that is a fundamental component of the local ecosystem. The emergence of new human marine activities and infrastructure, such as tidal turbine installations, introduces new sound sources that change or disrupt the existing acoustic environment, but the full extent of these changes is not well understood and is not predictable. To better evaluate the effects of future tidal energy development on the local soundscape in Grand Passage, Nova Scotia, a thorough understanding of the pre-development characteristics must be established. The present research seeks to identify existing anthropophony, biophony, and geophony, and to evaluate the underlying mechanisms contributing to geophonic variability. Passive acoustic measurements have been conducted using long-term moored omnidirectional hydrophones, a moored 5-channel array, and drifting hydrophone arrays, enabling identification of dominant signals and source direction, estimation of pseudonoise masking effects due to turbulent flow, and analysis of diurnal, daily, seasonal, and annual variability as well as spatial variability within the study area. This paper provides an overview of the dominant sound sources and discusses the effects of environmental forcing on soundscape characteristics.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.201
Teacher spread0.194 · 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.

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

Citations2
Published2016
Admission routes2
Has abstractyes

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