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Record W2078261573 · doi:10.1109/icassp.2013.6637749

Single hydrophone passive acoustic sperm whale range and depth estimation

2013· article· en· W2078261573 on OpenAlexaff
Hannan Lohrasbipeydeh, Saeed Mosayyebpour, T. Aaron Gulliver

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSperm whaleMultilaterationHuman echolocationAcousticsHydrophoneWhaleBioacousticsSpermComputer scienceRange (aeronautics)GeologyEngineeringBiologyPhysicsFishery

Abstract

fetched live from OpenAlex

Sperm whales (Physeter macrocephalus) emit acoustic signals when diving to search for food. Because they dive to depths of over 1500 m, conventional localization methods are impractical. Sperm whales produce a series of loud impulsive echolocation sounds called `clicks' at a fairly constant rate, and these signals can be used for localization. A geometric approach is considered using signals from the direct and surface reflection paths. The time difference of arrival (TDOA) between these paths is used for localization. Real sperm whale data from the Atlantic Undersea Test Center (AUTC) is used to evaluate the proposed method.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.999

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)0.0090.002

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 head0.021
GPT teacher head0.229
Teacher spread0.208 · 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 designSimulation or modeling
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

Citations7
Published2013
Admission routes1
Has abstractyes

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