Classifying synthetic and biological DNA sequences with side effect machines
Bibliographic record
Abstract
Finite state machines are routinely used to efficiently recognize patterns in strings. The internal state structure of the machine is typically only of peripheral interest, appearing in algorithms only when the number of states is minimized in the interests of efficiency of execution or comparison. A side effect machine saves information about the internal transitions of the state machine. This record of internal state transitions forms an induced feature set for any string run through the side effect machine. In this study the number of times a machine passes though each state is used as a numerical feature set for classification. Finite state machines are trained with an evolutionary algorithm to produce feature sets that are very easy for an unsupervised learning algorithm, k-means clustering, to learn. The system is demonstrated on synthetic and biological data. The biological data are PCR-primers classified by their success at amplification. The parameters, number of states, population size, and mutation rates are explored to characterize their effect on performance. Side effect machines are found to be effective at recognizing classes of DNA sequence data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".