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Record W2954261501 · doi:10.1093/plankt/fbz016

Seasonal zooplankton development in a temperate semi-enclosed basin: two years with different spring bloom timing

2018· article· en· W2954261501 on OpenAlexafffundabout
Natalie Mahara, Evgeny A. Pakhomov, Jennifer M. Jackson, Brian P. V. Hunt

Bibliographic record

VenueJournal of Plankton Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaHakai InstituteTula Foundation
KeywordsZooplanktonBloomSpring bloomPhenologyEnvironmental scienceOceanographyTemperate climateEcologyEcological successionBiomass (ecology)PopulationBiologyPhytoplanktonNutrientGeologyDemography

Abstract

fetched live from OpenAlex

Abstract Coastal temperate waters undergo considerable intra- and interannual environmental variations, which is reflected in the dynamic nature of their zooplankton communities. Since zooplankton phenology is dependent on several factors, particularly temperature and spring bloom timing, it is imperative to understand how zooplankton communities may shift under future climate conditions with warmer temperatures and more variable spring bloom initiation. To examine zooplankton phenology and response to shifts in bloom timing, we analyzed fortnightly zooplankton and environmental samples collected in the northern Strait of Georgia (B.C., Canada), a large semi-enclosed temperate basin, in 2015 and 2016. Despite a 5-week difference in spring bloom timing, zooplankton community succession was remarkably similar between years. In both years, biomass peaked within the same calendar week and communities were separated into winter, early spring and summer-autumn assemblages that formed independent of the spring bloom timing. Although some species-level phenological differences were observed between years, predominately delayed population development, zooplankton communities appeared to demonstrate resilience to interannual environmental variations on the whole. If ongoing warming shifts the timing of zooplankton consumers’ life history timing, it could lead to a mismatch with their zooplankton prey resource that exhibits comparatively less interannual variability.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.043
GPT teacher head0.311
Teacher spread0.268 · 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.

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

Citations20
Published2018
Admission routes3
Has abstractyes

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