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Record W2134641446

Feature-Aided Tracking for Marine Mammal Detection and Classification

2008· article· en· W2134641446 on OpenAlexvenueno aff
Odile Gérard, Craig Carthel, Stefano Coraluppi, Peter Willett

Bibliographic record

VenueCanadian acoustics · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
FundersNaval Undersea Warfare CenterOcean Life Institute, Woods Hole Oceanographic InstitutionWoods Hole Oceanographic Institution
KeywordsBeaked whaleHuman echolocationComputer scienceWhaleBioacousticsMarine mammalFeature (linguistics)Artificial intelligenceSperm whaleTracking (education)Pattern recognition (psychology)Speech recognitionAcousticsBiologyEcologyTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

This paper presents a method to detect and classify odontocete echolocation clicks as well as to estimate the number of animals that are vocalizing.A transient detector using the Page test [1][2][3] is used to extract the clicks: the click time, the click duration, the click amplitude and the spectral information of the clicks are extracted.A probability distribution over the species is assigned to each click, based on the spectral information of the click.The estimation of the number of animals is done using feature-aided multi hypothesis tracking (MHT) algorithms.The association is based on the assumptions of slowly-varying click amplitude and intra-click timing [4][5].This work has been done on the dataset provided by the organizers of the 3rd International Workshop on the Detection and Classification o f Marine Mammals using Passive Acoustics, Boston, July 2007.This dataset consists of training and test data; the training data includes vocalizations of three species: Blainville's beaked whale (Mesoplodon densirostris), Risso's dolphin (Grampus griseus) and short-finned pilot whale (Globicephala macrorhynchus).s o m m a i r e Cet article prsente une mthode de dtection et classification de clics d 'cholocation d 'odontoctes ainsi que d'estimation du nombre d 'animaux vocalisant en mme temps.Un dtecteur de transitoires utilisant le test de Page [1-3] permet d 'extraire les clics : leurs instants, dures et amplitudes ainsi que leurs spectres sont stocks.L 'analyse du spectre d 'un clic permet de lui affecter une probabilit de distribution parmi les diffrentes espces.L 'estimation du nombre d 'animaux se fait l'aide d 'un algorithme de tracking (multi hypothesis tracking MHT).L 'association des clics est base sur l'hypothse que l 'amplitude et l'intervalle entre deux clics varient lentement en fonction du temps.Ce travail a t ralis sur le jeu de donnes mis disposition par les organisateurs du 3rd International Workshop on the Detection and Classification o f Marine Mammals using Passive Acoustics, Boston, Juillet 2007.Ce dernier se compose de donnes d 'entrainement sur trois espces : Msoplodon de Blainville (Mesoplodon densirostris), dauphins de Risso (Grampus griseus) et globicphales (Globicephala macrorhynchus) et de fichiers test.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.979

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 spread0.193 · 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

Citations11
Published2008
Admission routes1
Has abstractyes

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