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Record W3014082623 · doi:10.1139/as-2019-0022

The summer soundscape of a shallow-water estuary used by beluga whales in the western Canadian Arctic

2020· article· en· W3014082623 on OpenAlexaffvenueabout
William D. Halliday, Kevin Scharffenberg, Dustin Whalen, Shannon A. MacPhee, Lisa L. Loseto, Stephen J. Insley

Bibliographic record

VenueArctic Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsNatural Resources CanadaWildlife Conservation Society CanadaUniversity of ManitobaGovernment of CanadaFisheries and Oceans CanadaUniversity of Victoria
Fundersnot available
KeywordsBeluga WhaleSoundscapeBelugaEstuaryArcticOceanographyLeucasEnvironmental scienceHabitatSound (geography)Marine habitatsFisheryGeographyGeologyEcologyBiology

Abstract

fetched live from OpenAlex

The soundscape is an important habitat component for marine animals. In the Arctic, marine conditions are changing rapidly due to sea ice loss and increased anthropogenic activities such as shipping, which will influence the soundscape. Here, we assess the contributors to the summer soundscape in the shallow waters of the Mackenzie River estuary within the Tarium Niryutait Marine Protected Area in the western Canadian Arctic, a core summering habitat for beluga whales (Delphinapterus leucas Pallas, 1776). We collected passive acoustic data during the summer over four years, and assessed the influence of physical variables, beluga whale vocalizations, and boat noise on sound pressure levels in three frequency bands (low: 0.2–1 kHz; medium: 1–10 kHz; high: 10–48 kHz) to quantify the soundscape. Wind speed, wave height, beluga vocalizations, and boat noise were all large contributors to the soundscape in various frequency bands. The soundscape varied to a lesser degree between sites, time of day, and with tide height, but remained relatively constant between years. This study is the first detailed description of a shallow summer soundscape in the western Canadian Arctic, an important habitat for beluga whales, and can be used as a baseline to monitor future changes during this season.

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 score0.256
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.029
GPT teacher head0.239
Teacher spread0.209 · 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

Citations14
Published2020
Admission routes3
Has abstractyes

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