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Record W2014012329 · doi:10.1121/1.4783896

<i>In situ</i> and experimental observations of the relationships between euphausiid orientation, vessel lights, and acoustical scattering

2004· article· en· W2014012329 on OpenAlexaff
Mark C. Benfield, Michelle L. Ashton, Mark V. Trevorrow, David L. Mackas

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans CanadaDefence Research and Development Canada
Fundersnot available
KeywordsScatteringZooplanktonGeologyOceanographyOpticsFjordOrientation (vector space)AnisotropyPhysicsAcousticsGeometry

Abstract

fetched live from OpenAlex

During a collaborative investigation of zooplankton aggregations near a coastal fjord (Knight Inlet, British Columbia), surveys were conducted using down-looking echosounders and a digital imaging system (ZOOVIS). Two surveys conducted at night revealed that the presence or absence of external illumination on the vessel had a pronounced influence on measured acoustical scattering. The extremely short latency between shifts in illumination and changes in acoustical scattering suggested that differences in the orientations of scatterers were responsible for this phenomenon. High-resolution, in situ images of euphausiids (Euphausia pacifica) from ZOOVIS indicated that these organisms were present in a range of orientations ranging from horizontal to vertical, relative to the incident acoustical beam. Theoretical scattering models based on digitizations of in situ ZOOVIS images suggest that the magnitude of the observed changes in scattering, in response to altered illumination, may be accounted for by changes in euphausiid orientation. [Research supported by the ONR, Code 322BC.]

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.028
GPT teacher head0.270
Teacher spread0.242 · 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
Published2004
Admission routes1
Has abstractyes

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