Classifying synthetic and biological DNA sequences with side effect machines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it