MétaCan
Menu
Back to cohort
Record W3174635398 · doi:10.1080/23802359.2021.1923426

Mitochondrial genome and phylogenomic analysis of <i>Pseudo-nitzschia micropora</i> (Bacillariophyceae, Bacillariophyta)

2021· article· it· W3174635398 on OpenAlexaff
Yang Chen, Zongmei Cui, Feng Liu, Nansheng Chen

Bibliographic record

VenueMitochondrial DNA Part B · 2021
Typearticle
Languageit
FieldEnvironmental Science
TopicMarine Toxins and Detection Methods
Canadian institutionsSimon Fraser University
FundersMajor Scientific and Technological Innovation Project of Shandong ProvinceChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsBiologyDomoic acidPhylogenetic treeGenomeMitochondrial DNATransfer RNAGeneNitzschiaRibosomal RNAGeneticsGC-contentEvolutionary biologyRNAEcologyPhytoplankton

Abstract

fetched live from OpenAlex

The number of species in the genus Pseudo-nitzschia has increased to 56, including 26 species known to produce domoic acid (DA), which is harmful to marine animals and human health. The lack of genomic sequences of Pseudo-nitzschia species has been a limiting factor in the studies of genetic and evolutionary relationships of Pseudo-nitzschia species. Here, the complete mitochondrial genome sequence of Pseudo-nitzschia micropora was determined for the first time, which was 38,792 bp in length with the overall AT content being 69.98%. The mitochondrial genome encoded 62 genes, including 36 protein-coding genes (PCGs, including orf157), 24 transfer RNA (tRNA) genes and two ribosomal RNA (rRNA) genes. Phylogenetic tree analysis suggests that the P. micropora had a closer relationship with P. cuspidate than that with P. multiseries. The availability of the complete mitochondrial genome of P. micropora would be useful for researching the evolutionary relationships of Pseudo-nitzschia species.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueMitochondrial DNA Part BSame topicMarine Toxins and Detection MethodsFrench-language works237,207