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Record W2141661035 · doi:10.1139/f03-065

Distributions of physonect siphonulae in the Gulf of Maine and their potential as important sources of acoustic scattering

2003· article· en· W2141661035 on OpenAlexvenueno aff
Mark C. Benfield, Andone C. Lavery, Peter H. Wiebe, Charles H. Greene, Timothy K. Stanton, Nancy Copley

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Invertebrate Physiology and Ecology
Canadian institutionsnot available
FundersOffice of Naval ResearchNational Oceanic and Atmospheric AdministrationWoods Hole Oceanographic Institution
KeywordsBackscatter (email)GeologyNeutral buoyancyBuoyancyRange (aeronautics)Volume (thermodynamics)OceanographyScatteringAerial rootEcho soundingPlanktonRemote sensingEnvironmental scienceMangrovePhysicsOpticsMaterials scienceBiologyFishery

Abstract

fetched live from OpenAlex

The distributions of siphonulae stages of physonect siphonophores were mapped in Wilkinson, Jordan, and Georges basins of the Gulf of Maine using a video plankton recorder. Siphonulae are often overlooked in net samples and our optical survey appears to be the first in situ investigation of these organisms. Siphonulae were distributed at mid-depths in narrow horizontal layers, suggesting potential control of their buoyancy. The siphonulae possessed gas-filled pneumatophores with diameters of 0.1–0.4 mm. Pneumatophore diameters appeared to be similar over their entire sampled depth range, suggesting that siphonulae may be capable of regulating the pressure of gas within the pneumatophore to maintain a constant volume. The dimensions of the siphonulae pneumatophores placed them near the acoustic resonance region for scattering at 43 kHz when near the surface and 120 kHz when at depth. Theoretical estimates of the acoustical target strengths of gas bubbles of sizes corresponding to the sizes of our measured pneumatophores produced relatively strong backscatter. Layers of siphonulae corresponded to regions of high acoustical backscatter at 120, 200, and 420 kHz.

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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.988

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 studies0.0000.001
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 head0.010
GPT teacher head0.186
Teacher spread0.176 · 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 teacher head, 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

Citations37
Published2003
Admission routes1
Has abstractyes

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