Bibliographic record
Abstract
The problem of controlling errors in the process of molecular self-assembly is of central importance in biomolecular computation. The stochastic nature of the self-assembly process leads to assembly errors, i.e., deviations from ideal growth of the assembly. Many different constructions of proofreading tile sets have been proposed in the literature to reduce the effect of such assembly errors. Another major error mechanism affecting the self-assembly process in practice is that of imperfections within the tiles themselves. This source of error has, surprisingly, received little prior attention. In this work, we consider a scenario in which some small proportion of the tiles in a tile set are ¿malformed¿. We study, through simulations, the effect of such malformed tiles on the self-assembly process within the kinetic Tile Assembly Model (kTAM). The simulation results show that some tile set constructions show greater error-resilience in the presence of malformed tiles than others. But, most notably, the snaked proofreading tile set of Chen and Goel fails to form even moderately-sized tile assemblies when malformed tiles are present. We present modifications of the snaked proofreading construction that indicate that it is possible to design tile sets that are not just robust with respect to errors intrinsic to the self-assembly process, but also with respect to malformed tiles.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".