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Record W1963551872 · doi:10.1139/f08-179

Acoustic characterization of Mysis relicta at multiple frequencies

2008· article· en· W1963551872 on OpenAlexvenueno aff
Lars G. Rudstam, Frank Reier Knudsen, Helge Balk, Gideon Gal, Brent T. Boscarino, Thomas Axenrot

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTarget strengthRange (aeronautics)ScatteringAcousticsMaterials sciencePhysicsFish <Actinopterygii>Environmental scienceOpticsBiologyFishery

Abstract

fetched live from OpenAlex

We measured acoustic backscattering from Mysis relicta , a common invertebrate in northern lakes, using five frequencies (38, 120, 200, 430, and 710 kHz). Acoustic backscattering from mysids was highest at 430 kHz and lowest at 38 kHz (19 dB lower). Maximum difference between the four other frequencies was 5.2 dB. Mysid target strength (TS) ranged from –80.1 dB at 430 kHz to –99.4 dB at 38 kHz (12 mm average length, range 5–21 mm). A theoretical scattering model (Stanton’s fluid-like, bent-cylinder model) predicted TS within 0.3–1.9 dB of observed TS for the different frequencies. The detection range was lowest at 38 and 710 kHz and greatest at 120 and 200 kHz. Fish were common above the mysid layer and produced higher acoustic backscattering at 38 kHz than at the other frequencies. A combination of 38 kHz and 120 or 200 kHz provides a strong contrast between mysid and fish acoustic backscattering that would help separate these groups using acoustic data.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.186
Teacher spread0.168 · 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

Citations25
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicFish Ecology and Management Studies→French-language works237,207→