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Automated acoustic tracking of aquatic animals: scales, design and deployment of listening station arrays

2006· article· en· 626 citations· W2109240323 on OpenAlex· 10.1071/mf05091

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.

Full frame distilled prediction

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.

Candidate categories
none
Consensus categories
none
Domain
Candidate signal: noneConsensus signal: none
Study design
Candidate signal: ObservationalConsensus signal: Observational
Genre
Candidate signal: EmpiricalConsensus signal: Empirical
Teacher disagreement score
0.307
Threshold uncertainty score
0.997
Validation status
machine_predicted_unvalidated · codex-gemma-dda1882f352a

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

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.

Opus teacher head0.055
GPT teacher head0.310
Teacher spread
0.255 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

The recent introduction of low-cost, moored data-logging acoustic receivers has provided opportunities for tracking marine organisms over small (hundreds of metres) and large scales (hundreds of kilometres). Acoustic receivers have been deployed in many different environments to examine specific hypotheses regarding the movement of aquatic species. This technology provides many advantages for studying aquatic animal movement patterns, but also has limitations and provides unique difficulties for users. Study design, applications, advantages and limitations are discussed with examples from past and current studies. Data management and analysis techniques are in their infancy and few standardised techniques exist. Complications with data management and potential data analysis techniques are discussed. Examples from the literature are utilised wherever possible to provide useful references.

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.

The record

Venue
Marine and Freshwater Research
Topic
Marine animal studies overview
Field
Environmental Science
Canadian institutions
Vancouver Aquarium
Funders
not available
Keywords
Software deploymentComputer scienceData scienceData loggerBioacousticsTracking (education)TelecommunicationsSoftware engineering
Has abstract in OpenAlex
yes