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Record W3093053151 · doi:10.1111/jeu.12827

Microeukaryotic Communities Associated With the Seagrass <i>Zostera marina</i> Are Spatially Structured

2020· article· en· W3093053151 on OpenAlexafffund
Bianca Trevizan Segóvia, Rhea D. Sanders, Emily M. Adamczyk, Coreen Forbes, Margot Hessing‐Lewis, Mary I. O’Connor, Laura Wegener Parfrey

Bibliographic record

VenueJournal of Eukaryotic Microbiology · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal plant biology
Canadian institutionsTula FoundationUniversity of British Columbia
FundersHakai InstituteMitacsCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaTula Foundation
KeywordsSeagrassZostera marinaPlanktonAbiotic componentTrophic levelEcologyBiologyEcosystem

Abstract

fetched live from OpenAlex

Epibiotic microorganisms link seagrass productivity to higher trophic levels, but little is known about the processes structuring these communities, and which taxa consistently associate with seagrass. We investigated epibiotic microeukaryotes on seagrass (Zostera marina) leaves, substrates, and planktonic microeukaryotes in ten meadows in the Northeast Pacific. Seagrass epibiotic communities are distinct from planktonic and substrate communities. We found sixteen core microeukaryotes, including dinoflagellates, diatoms, and saprotrophic stramenopiles. Some likely use seagrass leaves as a substrate, others for grazing, or they may be saprotrophic organisms involved in seagrass decomposition or parasites; their relatives have been previously reported from marine sediments and in association with other hosts such as seaweeds. Core microeukaryotes were spatially structured, and none were ubiquitous across meadows. Seagrass epibiota were more spatially structured than planktonic communities, mostly due to spatial distance and changes in abiotic conditions across space. Seawater communities were relatively more similar in composition across sites and more influenced by the environmental component, but more variable over time. Core and transient taxa were both mostly structured by spatial distance and the abiotic environment, with little effect of host attributes, further indicating that those core taxa would not show a strong specific association with Z. marina.

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)0.000
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 score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.176
Teacher spread0.161 · 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

Citations22
Published2020
Admission routes2
Has abstractyes

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