Phylogeny and species delineation in the marine diatom<i>Pseudo‐nitzschia</i>(Bacillariophyta) using<i>cox1</i>,<scp>LSU</scp>, and<scp>ITS</scp>2<scp>rRNA</scp>genes: A perspective in character evolution
Bibliographic record
Abstract
Analyses of the mitochondrial cox1, the nuclear-encoded large subunit (LSU), and the internal transcribed spacer 2 (ITS2) RNA coding region of Pseudo-nitzschia revealed that the P. pseudodelicatissima complex can be phylogenetically grouped into three distinct clades (Groups I-III), while the P. delicatissima complex forms another distinct clade (Group IV) in both the LSU and ITS2 phylogenetic trees. It was elucidated that comprehensive taxon sampling (sampling of sequences), selection of appropriate target genes and outgroup, and alignment strategies influenced the phylogenetic accuracy. Based on the genetic divergence, ITS2 resulted in the most resolved trees, followed by cox1 and LSU. The morphological characters available for Pseudo-nitzschia, although limited in number, were overall in agreement with the phylogenies when mapped onto the ITS2 tree. Information on the presence/absence of a central nodule, number of rows of poroids in each stria, and of sectors dividing the poroids mapped onto the ITS2 tree revealed the evolution of the recently diverged species. The morphologically based species complexes showed evolutionary relevance in agreement with molecular phylogeny inferred from ITS2 sequence-structure data. The data set of the hypervariable region of ITS2 improved the phylogenetic inference compared to the cox1 and LSU data sets. The taxonomic status of P. cuspidata and P. pseudodelicatissima requires further elucidation.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".