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Record W2017069111 · doi:10.1109/oceans.2014.7003266

Passive energy based acoustic signal analysis for diver detection

2014· article· en· W2017069111 on OpenAlex

Why this work is in the frame

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

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed Sensor Networks and Detection Algorithms
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsEnergy (signal processing)SIGNAL (programming language)Spectral densityDetectorAcousticsComputer scienceDetection theorySignal processingTime–frequency analysisBioacousticsTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

A passive energy based analysis of the acoustic signal from a diver is presented. The frequency components of the signal during inhaling and exhaling are investigated, as well as the periodicity of the signal features. The effect of different regulators on the power spectral density of the signals is evaluated. The energy based detection problem is formulated using the time-frequency Teager Energy Operator (TEO) as it can be used to develop an adaptive detector. To evaluate the proposed approach, experimental data is employed which was recorded in the open ocean at different ranges and depths. This data was obtained using accurate hydrophones provided by Ocean Network Canada (ONC).

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.201
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