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Record W1978115949 · doi:10.1121/1.1568943

Patterns in the vocalizations of male harbor seals

2003· article· en· W1978115949 on OpenAlexaff
Sofie M. Van Parijs, Peter Corkeron, James T. Harvey, Sean A. Hayes, David K. Mellinger, Philippe A. Rouget, Paul M. Thompson, Magnus Wahlberg, Kit M. Kovacs

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsBamfield Marine Sciences CentreUniversity of Victoria
FundersOffice of Naval ResearchEuropean CommissionDavid and Lucile Packard Foundation
KeywordsVariation (astronomy)Regional variationPopulationGeographyGeographic variationBiologyEcologyEvolutionary biologyDemography

Abstract

fetched live from OpenAlex

Comparative analyses of the roar vocalization of male harbor seals from ten sites throughout their distribution showed that vocal variation occurs at the oceanic, regional, population, and subpopulation level. Genetic barriers based on the physical distance between harbor seal populations present a likely explanation for some of the observed vocal variation. However, site-specific vocal variations were present between genetically mixed subpopulations in California. A tree-based classification analysis grouped Scottish populations together with eastern Pacific sites, rather than amongst Atlantic sites as would be expected if variation was based purely on genetics. Lastly, within the classification tree no individual vocal parameter was consistently responsible for consecutive splits between geographic sites. Combined, these factors suggest that site-specific variation influences the development of vocal structure in harbor seals and these factors may provide evidence for the occurrence of vocal dialects.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.249
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 source (direct Gemma or distilled Codex), 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

Citations51
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicMarine animal studies overviewFrench-language works237,207