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Record W6998404966

Acoustic monitoring of beluga whales (Delphinapterus leucas): spatio-temporal habitat preference and geographic variation in Canadian populations

2018· dissertation· en· W6998404966 on OpenAlexfundaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2018
Typedissertation
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersFisheries and Oceans CanadaNatural Sciences and Engineering Research Council of CanadaArcticNetQuark ExpeditionsWorld Wildlife Fund
KeywordsBeluga WhaleBelugaDiel vertical migrationHabitatVariation (astronomy)Aerial surveyPreferenceSound (geography)Biotelemetry
DOInot available

Abstract

fetched live from OpenAlex

Acoustic monitoring is an effective means by which to study cetaceans, such as beluga whales (Delphinapterus leucas), and can be useful in determining habitat preference and geographic variation among populations. Acoustic monitoring data were analyzed using a combination of automated detection and manual analysis to determine habitat preference of Cumberland Sound beluga in their summering range. Belugas were primarily detected in the northernmost site in Clearwater Fiord, with diel variation in call patterns at two separate sites in different years. No correlation was evident between tidal cycles and beluga detections. A second study examined geographic variation in simple contact calls (SCC’s) among four Canadian beluga populations. Results indicate variation in the measured parameters (duration, peak frequency and pulse repetition rate) among four populations and align with genetic variation previously described in the literature. These findings provide important information necessary for the conservation and management of beluga populations in Canada.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
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 head0.031
GPT teacher head0.223
Teacher spread0.192 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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