A case study to estimate the applicability of secondary structures of<scp>SSU</scp>‐<scp>rRNA</scp>gene in taxonomy and phylogenetic analyses of ciliates
Bibliographic record
Abstract
Phylogenetic studies of ciliates are mainly based on the primary structure information of the nuclear genes. Some regions of the small subunit ribosomal RNA ( SSU ‐ rRNA ) gene have distinctive secondary structures, which have demonstrated value as phylogenetic/taxonomic characters. In the current work, we predict the secondary structures of four variable regions (V2, V4, V7 and V9) in the SSU ‐ rRNA gene of 45 urostylids. Structure comparisons indicate that the V4 region is the most effective in revealing interspecific relationships, while the V9 region appears suitable at the family level or higher. The V2 region also offers some taxonomic information, but is too conserved to reflect phylogenetic relationships at the family or lower level, at least for urostylids. The V7 region is the least informative. We constructed several phylogenetic trees, based on the primary sequence alignment and based on an improved alignment according to the secondary structures. The results suggest that including secondary structure information in phylogenetic analyses provides additional insights into phylogenetic relationships. Using urostylid ciliates as an example, we show that secondary structure information results in a better understanding of their relationships, for example generic relationships within the family Pseudokeronopsidae.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".