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Record W1573304734

Underwater acoustic localization of marine mammals in an uncertain environment

2009· article· en· W1573304734 on OpenAlexaffvenue
B. Rideout, Stan E. Dosso, David Hannay

Bibliographic record

VenueCanadian acoustics · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsUnderwaterPopulationHabitatMarine habitatsOceanographyGeologyEnvironmental scienceComputer scienceEcologyBiology
DOInot available

Abstract

fetched live from OpenAlex

An MSc thesis research project aimed at investigating three-dimensional acoustic localization and tracking of vocalizing marine mammals. The main objective of the research project was to provide a more reliable method for assessing temporal variations in walrus population distributions and habitat usage. Investigations revealed that significant changes in the degree of summer ice coverage in the Chukchi Sea along with increased seismic activity in the waters surrounding Alaska had the potential to impact marine mammals, including walruses. The Pacific Walrus was selected for conducting the localization study due to their presence in the investigated area. The study considered two approaches to investigate acoustic localization of the mammals. The first approach used several independent data acquisition systems, while the second approach involved evaluation of a single data acquisition system for three-dimensional acoustic localization.

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 score0.071
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.016
GPT teacher head0.223
Teacher spread0.207 · 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

Citations0
Published2009
Admission routes2
Has abstractyes

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