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Record W4400847784 · doi:10.1093/plankt/fbae039

Spring resting egg production of the calanoid copepod, <i>Eurytemora affinis</i>, in a freshet-dominated estuary

2024· article· en· W4400847784 on OpenAlexafffundabout
Joanne Breckenridge, Evgeny A. Pakhomov

Bibliographic record

VenueJournal of Plankton Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOverwinteringCopepodBiologyEstuaryPopulationDiapauseEcologyAbundance (ecology)CrustaceanLarva

Abstract

fetched live from OpenAlex

Abstract Seasonal peaks in river discharge, such as snowmelt-dominated freshets, are predictable events that can have a large effect on flushing rates and salinity in estuaries. Resting eggs, which many coastal and estuarine copepods produce for overwintering or aestivation, could also serve to bridge predictable peaks in river discharge. We assessed the timing of resting egg production of the egg-carrying estuarine copepod, Eurytemora affinis (Poppe), in relation to river discharge in the Fraser River Estuary, Canada. Approximately 30 field-collected females were individually incubated on 12 occasions over the period February 2015–May 2016. Eurytemora affinis abundance and population structure were investigated from vertical net tow samples collected twice monthly to monthly. Resting eggs occurred primarily in May 2015 and May 2016 (6.5 and 9.2 eggs day−1, respectively), a month prior to peak flows, and the proportion of offspring that were resting eggs increased with river discharge. Eurytemora affinis reached a minimum abundance in July 2015, when the population was dominated by adults (86%). Resting egg production in E. affinis is typically considered an overwintering mechanism but we suggest that the ultimate driver of resting egg production in this population is avoidance of flushing and/or low salinities.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.195
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.035
GPT teacher head0.316
Teacher spread0.281 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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