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Record W4406750095 · doi:10.1139/as-2024-0016

The community of marine alveolate parasites in the Atlantic inflow to the Arctic Ocean is structured by season, depth, and water mass

2025· article· en· W4406750095 on OpenAlexvenueno aff
Elianne Egge, Daniel Vaulot, Aud Larsen, Bente Edvardsen

Bibliographic record

VenueArctic Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtist diversity and phylogeny
Canadian institutionsnot available
FundersNorges ForskningsrådAgence Nationale de la Recherche
KeywordsInflowOceanographyArcticThe arcticWater massEnvironmental scienceCurrent (fluid)GeographyGeology

Abstract

fetched live from OpenAlex

The marine alveolates (MALVs) are a highly diverse group of parasitic dinoflagellates, which may regulate populations of a wide range of hosts, including other dinoflagellates, copepods, and fish eggs. Knowledge on their distribution and ecological role is still limited, as they are difficult to study with morphological methods. In this work, we describe the taxonomic composition and seasonal and depth distribution of MALVs in the Arctic Ocean west and north of Svalbard, based on 18S V4 rRNA metabarcoding data from five cruises. We recovered amplicon sequence variants (ASVs) representing all major groups previously described from environmental sequencing studies (Dino-Groups I–V), with Dino-Groups I and II being the most diverse. The community was structured by season, depth, and water mass. In the epipelagic zone, the taxonomic composition varied strongly by season; however, there was also a difference between Arctic and Atlantic water masses in winter. The spring and summer epipelagic communities were characterized by a few dominating ASVs present in low proportions during winter and in mesopelagic summer samples, suggesting that they proliferate under certain conditions, e.g., when specific hosts are abundant. Mesopelagic samples were more similar across months, and may harbor parasites of deep-dwelling organisms, little affected by season.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.431

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 studies0.0010.001
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 head0.007
GPT teacher head0.238
Teacher spread0.232 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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