AN ALGORITHM FOR THE ENERGY BARRIER PROBLEM WITHOUT PSEUDOKNOTS AND TEMPORARY ARCS
Bibliographic record
Abstract
We make two new contributions to the problem of calculating pseudoknot-free folding pathways with minimum energy barrier between pairs (Α,Β) of RNA secondary structures. Our first contribution pertains to a problem posed by Morgan and Higgs: find a min-barrier direct folding pathway for a simple energy model in which each base pair contributes -1. In a direct folding pathway, intermediate structures contain only base pairs in Α and Β and are of length |AΔB| (the size of the symmetric difference of the two structures). We show how to solve this problem exactly, using techniques for deconstructing bipartite graphs. The problem is NP-hard and so our algorithm requires exponential time in the worst case but performs quite well empirically on pairs of structures that are hundreds of nucleotides long. Our second contribution shows that for the simple energy model, repeatedly adding or removing a base pair from A U B along a pathway is not useful in minimizing the energy barrier. Two consequences of this result are that (i) the problem of determining the min-barrier pseudoknot-free folding pathway from the space of direct pathways with repeats is NP-hard and (ii) our new algorithm finds the min-barrier pathway not only from the space of direct folding pathways but in fact from the space of direct pathways with repeats.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".