Bibliographic record
Abstract
Single-stranded RNA plant viruses not only code for viral proteins within their RNA genomes, they often maintain elaborate RNA secondary structures. These structures can be integral to a variety of viral processes, such as viral translation, genome replication, subgenomic mRNA transcription, and genome encapsidation. RNA secondary structures may function to recruit and bind trans-acting protein factors, or may become part of higher order tertiary RNA structures, which themselves may be functionally relevant. To fully understand such viral RNA elements and their mechanisms of action, it is necessary to first determine their secondary structures.Computer modeling based on free energy minimizing principles is generally used as an initial approach to predict potential RNA secondary structures in a sequence. The most popular program is mfold, which is available for free to the public at http://www.bioinfo.rpi.edu/applications/mfold/old/rna/form1.cgi (1). Though useful starting points, the mfold-predicted structures must be confirmed by more direct experimental approaches. Solution structure probing of RNA is a commonly utilized technique that provides information regarding the secondary structure of an RNA primary sequence in solution. This process involves treating the RNA of interest with enzymes or chemicals that modify RNA differentially based on its secondary structure. The modified sites, at either single- or double-stranded regions, can be subsequently identified by primer extension and gel electrophoresis. Data from solution structure probing experiments can be superimposed onto a computer-predicted structure to further help confirm or refine the predicted RNA secondary structure model.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".