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Record W2117798392 · doi:10.1111/fog.12072

Modelling the dispersal of herring and hake larvae in the Strait of Georgia for the period 2007–2009

2014· article· en· W2117798392 on OpenAlexafffundabout
Evgeniya Lyubomirova Snauffer, Diane Masson, Susan E. Allen

Bibliographic record

VenueFisheries Oceanography · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsFisheries and Oceans CanadaUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHerringBiological dispersalOcean gyreOceanographyFisheryLarvaAtlantic herringPacific herringMarine larval ecologyIchthyoplanktonGlobal wind patternsGeographyBiologyGeologyClupeaEcologyFish <Actinopterygii>MeteorologyPopulation

Abstract

fetched live from OpenAlex

Abstract The Strait of Georgia (SoG), between Vancouver Island and mainland British Columbia, is a larval rearing ground for both hake and herring stocks, which are commercially important. Year‐to‐year variability in larval retention within the strait is examined by simulating drift tracks of larvae for these species using an ocean circulation model and a particle‐tracking model. Larvae with different vertical swimming behaviors were tracked in the springs of 2007, 2008, and 2009. Since herring larvae mostly stay near the surface, their distribution is heavily influenced by the wind. Strong winds to the north soon after the hatching period tend to wash herring larvae out of SoG and winds to the south help retain herring larvae inside the Strait. In 2007, the model indicates a massive wind‐driven export of herring larvae which may have led to the observed failure of herring production. In contrast, hake larvae reside deeper in the water column (50–200 m). Their distribution is less sensitive to surface forcing but is shaped by a deep gyre with cross‐strait currents. This study also suggests that the northern and southern SoG are weakly connected for herring larvae dispersal, which makes both regions potentially important to recruitment.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score0.523

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 science0.0010.000
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 head0.015
GPT teacher head0.218
Teacher spread0.202 · 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

Citations15
Published2014
Admission routes3
Has abstractyes

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