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Record W2096823262 · doi:10.1093/plankt/fbu004

Variability in the vertical distribution and advective transport of eight mesozooplankton taxa in spring in Rivers Inlet, British Columbia, Canada

2014· article· en· W2096823262 on OpenAlexafffundabout
Désirée Tommasi, Brian P. V. Hunt, Susan E. Allen, Rick Routledge, Evgeny A. Pakhomov

Bibliographic record

VenueJournal of Plankton Research · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
FundersHakai Institute
KeywordsAdvectionDiel vertical migrationZooplanktonCopepodPlanktonInletOceanographyPopulationEnvironmental scienceFjordHaloclineEcologyGeologyCrustaceanBiology

Abstract

fetched live from OpenAlex

Zooplankton vertical distribution data and velocity estimates from a hydrodynamic model were employed to determine zooplankton daily exchange during four cruises conducted from March to June 2010 in Rivers Inlet, a fjord in central British Columbia, Canada. Zooplankton transport rates varied temporally, being fastest in March when water velocities were highest. The active vertical movement of the zooplankton interacted with the vertically sheared flow field to influence zooplankton advection. Surface dwelling plankton such as Acartia longiremis and larvaceans experienced the highest advection losses of −0.4 and −0.5 day−1, respectively. In contrast, maximum advection losses of Paraeuchaeta elongata, a deeper dwelling copepod, were an order of magnitude lower, at 0.04 day−1. Transport rates varied by stage, extent of diel vertical migration and timing of ontogenetic migration. Advection rates were lower than literature-derived egg production rates, but comparable to literature-derived mortality rates. We suggest that advection may be a significant driver of population dynamics in years with similar rates of population growth. We also stress the importance of determining advection rates to obtain accurate estimates of vital rates.

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)0.007
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 score0.027
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.224
Teacher spread0.213 · 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

Citations9
Published2014
Admission routes3
Has abstractyes

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