RNA‐modifying machines in archaea
Bibliographic record
Abstract
It has been known for nearly half a century that coding and non-coding RNAs (mRNA, and tRNAs and rRNAs respectively) play critical roles in the process of information transfer from DNA to protein. What is both surprising and exciting, are the discoveries in the last decade that cells, particularly eukaryotic cells, contain a plethora of non-coding RNAs and that these RNAs can either possess catalytic activity or can function as integral components of dynamic ribonucleoprotein machines. These machines appear to mediate diverse, complex and essential processes such as intron excision, RNA modification and editing, protein targeting, DNA packaging, etc. Archaea have been shown to possess RNP complexes; some of these are authentic homologues of the eukaryotic complexes that function as machines in the processing, modification and assembly of rRNA into ribosomal subunits. Deciphering how these RNA-containing machines function will require a dissection and analysis of the component parts, an understanding of how the parts fit together and an ability to reassemble the parts into complexes that can function in vitro. This article summarizes our current knowledge about small-non-coding RNAs in Archaea, their roles in ribosome biogenesis and their relationships to the complexes that have been identified in eukaryotic cells.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".