Boolean Genetic Programming for Promoter Recognition in Eukaryotes
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
Fixed-length genetic programming is applied to the problem of promoter identification in eukaryotes. The goal is to generate solutions that can be easily interpreted and compared with known promoter characteristics. Using a Boolean function set applied to Boolean registers, inputs, and constant values, the approach builds a logical expression whose value gives the classification decision. Evaluated on a dataset of human promoters and non-promoters from coding regions, the approach is found to generate concise solutions that yield good specificity but poor sensitivity. Analysis of the programs that are generated indicates that a well-known, biologically significant, characteristic of promoter regions is successfully identified. Suggested future work involves implementing the system using fuzzy logic.
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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