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Record W4242474718 · doi:10.1121/1.4800968

Analysis of Northern bottlenose whale pulses and associated reflections recorded from the Gully Marine Protected Area

2013· article· en· W4242474718 on OpenAlexaff
Bruce Martin, Hilary Moors‐Murphy

Bibliographic record

VenueProceedings of meetings on acoustics · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsBedford Institute of Oceanography
Fundersnot available
KeywordsWhaleCanyonPopulationOceanographyGeologySampling (signal processing)Aerial surveyForagingFisheryGeographyRemote sensingFilter (signal processing)Geomorphology

Abstract

fetched live from OpenAlex

The Gully Marine Protected Area (MPA) is a large submarine canyon at the edge of the Scotian Shelf, south of Nova Scotia. A resident population of northern bottlenose whales are known to occur in the Gully throughout the year, and the canyon provides important foraging grounds for the population. Bottom-mounted Autonomous Multichannel Acoustic Recorders (AMAR) were deployed in the Gully for ten days in in March 2010 (sampling rate = 375 ksps) and two days in October 2011 (sampling rate = 128 ksps). Bisonar pulses produced northern bottlenose whales (likely used to echolocate prey) were recorded consistently throughout these AMAR deployments. The swept FM characteristics of the northern bottlenose whale pulses recorded were consistent over both years, and both data sets contained clear pulse reflections from bottom clutter or prey targets. In this paper we provide a description of the northern bottlenose whale pulses recorded in the Gully and make recommendations on short-time Fourier transform parameters for analysis of the pulses. A description of the pulse reflections is also provided, based on analysis of the reflection patterns using short-time Fourier transforms and by matched filtering with the direct arrival from the whales.

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)0.002
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 score0.191
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.243
Teacher spread0.219 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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