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Record W2020136402 · doi:10.1139/f06-157

Patterns of diel vertical migration of zooplankton in acoustic Doppler velocity and backscatter data on the Newfoundland Shelf

2006· article· en· W2020136402 on OpenAlexvenueaboutno aff
Nicholas R. Record, Brad de Young

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsDiel vertical migrationZooplanktonBackscatter (email)Acoustic Doppler current profilerOceanographyGeologyDoppler effectWater columnEcho soundingSpatial ecologyEnvironmental scienceCurrent (fluid)EcologyBiology

Abstract

fetched live from OpenAlex

Backscatter data from moored acoustic Doppler current profilers (ADCPs) are analysed to quantify the diel vertical migration patterns of zooplankton on the Newfoundland Shelf, Canada. Data from 11 moorings provide long time series (~100 days each) for in-depth statistical analysis. For one deployment, dry weight measurements of zooplankton are used to calibrate the acoustic backscatter. Quantification methods are developed and applied to the backscatter and vertical velocity time series to determine the characteristics of the observed diel migration. We show that the migration responds to changes in light intensity and water column temperature structure. We have sufficient spatial data to show high correlation of migration characteristics over spatial scales of up to tens of kilometres.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.899
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.239
Teacher spread0.210 · 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

Citations15
Published2006
Admission routes2
Has abstractyes

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