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Record W3112840771 · doi:10.21203/rs.3.rs-116685/v1

Evidences of Intra-Group Orca Call Rate Modulation Using A Small-Aperture Four Hydrophone Array

2020· preprint· en· W3112840771 on OpenAlexaboutno aff
Marion Poupard, Helena Symonds, Paul Spong, Hervé Glotin

Bibliographic record

VenueResearch Square (Research Square) · 2020
Typepreprint
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersCentre National de la Recherche ScientifiqueAgence Nationale de la Recherche
KeywordsHydrophoneAperture (computer memory)ShoreComputer scienceGeologyHabitatTelecommunicationsGeographyOceanographyAcousticsEcologyPhysicsBiology

Abstract

fetched live from OpenAlex

Abstract Acoustic emissions are vital to orcas ( Orcinus orca ) to socialize, hunt, orient, and maintain spatial awareness. In order to better analyze their inter and intra-group communication, we propose a novel protocol that allows us to associate vocalizations with their emitter (individual/matriline). Our approach is based on a low cost small-aperture four hydrophone array fixed near the shore up to a few km away from the orcas’ path, operated in conjunction with visual identification. It was conducted in the summer of 2019 off northern Vancouver Island, Canada, at the research station OrcaLab. A total of 722 calls were extracted and localized in azimuth via the hydrophone array from 3 case studies in which different events took place.We then calculated the Call Rate (CR) for each individual/matriline in order to describe their acoustic activity. Results show that CR is modulated according to the distance of the signaler from the joint group, the presence of another group, and the anthropic pressure (nearby cruise ship). This shows evidence of intertwined calls. This protocol does not interfere with the animals and opens new perspectives towards inter and intra-group communication analysis.

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)0.015
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.004
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0030.013
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.228
GPT teacher head0.403
Teacher spread0.175 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations0
Published2020
Admission routes1
Has abstractyes

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