Bibliographic record
Abstract
Computational Intelligence is frequently applied to solve RNA design problems, to construct RNA sequences that fold into biochemically useful structures and alignments. RNA Design's NP-Hardness means that heuristic solutions, such as Evolutionary algorithms' Simulated Annealing, are commonly used to more effectively search for RNA sequences that fold into the target structure. Examples of Simulated Annealing in RNA Design include SIMARD, the ERD approach, and RNAPredict, all which aim to return RNA Sequences as close as possible to the target structure. However, such methods only use a single simulated annealing cooling schedule even though literature covers many schedules with varied convergence and performances guarantees. Since existing RNA Design cooling schedule surveys only cover at most four RNA design problems over two simulated annealing variants, we investigate the performance of four major simulated annealing schedules with ten variants on twenty-nine RNA design sequences. Relevant findings include a) the insensitivity of geometric schedule parameters, b) that logarithmic cooling schedules can solve RNA Design problems not solved by other schedules, c) suggestions for adjusting geometric schedule stopping conditions, and d) identifying common issues in popular adaptive and non-adaptive schedules for RNA Design.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".