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Record W6888698222 · doi:10.21966/7t4p-s714

Data for the paper "Phylogenomic position of eupelagonemids, abundant, and diverse deep-ocean heterotrophs"

2022· dataset· en· W6888698222 on OpenAlexaff

Bibliographic record

VenueHakai Institute · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCladeRibosomal RNAGenePhylogeneticsGenomeSister group18S ribosomal RNA

Abstract

fetched live from OpenAlex

Single-cell RNA sequence data of two Eupelagonemid cells from Kwakshua Channel. Eupelagonemids, formerly known as Deep Sea Pelagic Diplonemids I (DSPD I), are among the most abundant and diverse heterotrophic protists in the deep ocean, but little else is known about their ecology, evolution, or biology in general. Originally recognized solely as a large clade of environmental ribosomal subunit RNA gene sequences (SSU rRNA), branching with a smaller sister group DSPD II, they were postulated to be diplonemids, a poorly-studied branch of Euglenozoa. Although new diplonemids have been cultivated and studied in depth in recent years, the lack of cultured eupelagonemids has limited data to a handful of light micrographs, partial SSU rRNA gene sequences, a small number of genes from single amplified genomes (SAGs), and only a single formal described species, Eupelagonema oceanica. To determine exactly where this clade goes in the tree of eukaryotes and begin to address the overall absence of biological information about this apparently ecologically important group, we conducted single-cell transcriptomics from two eupelagonemid cells. A SSU rRNA gene phylogeny shows these two cells represent distinct subclades within eupelagonemids, each different from E. oceanica. Phylogenomic analysis based on a 125-gene matrix contrasts with the findings based on ecological survey data, and shows eupelagonemids branch sister to the diplonemid subgroup Hemistasiidae.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0030.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.288
Teacher spread0.239 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2022
Admission routes1
Has abstractyes

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