RNAML: A standard syntax for exchanging RNA information
Bibliographic record
Abstract
Analyzing a single data set using multiple RNA informatics programs often requires a file format conversion between each pair of programs, significantly hampering productivity. To facilitate the interoperation of these programs, we propose a syntax to exchange basic RNA molecular information. This RNAML syntax allows for the storage and the exchange of information about RNA sequence and secondary and tertiary structures. The syntax permits the description of higher level information about the data including, but not restricted to, base pairs, base triples, and pseudoknots. A class-oriented approach allows us to represent data common to a given set of RNA molecules, such as a sequence alignment and a consensus secondary structure. Documentation about experiments and computations, as well as references to journals and external databases, are included in the syntax. The chief challenge in creating such a syntax was to determine the appropriate scope of usage and to ensure extensibility as new needs will arise. The syntax complies with the eXtensible Markup Language (XML) recommendations, a widely accepted standard for syntax specifications. In addition to the various generic packages that exist to read and interpret XML formats, an XML processor was developed and put in the open-source MC-Core library for nucleic acid and protein structure computer manipulation.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.025 |
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".