Optimized numerical mapping scheme for filter-based exon location in DNA using a quasi-Newton algorithm
Why this work is in the frame
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Bibliographic record
Abstract
An optimized numerical mapping scheme for achieving improved location of exons in DNA sequences using digital filters is proposed. Characteristic numerical values for the four nucleotides, referred to as pseudo-EIIP values, are obtained using a training procedure where the location accuracy is maximized using a quasi-Newton algorithm based on the Broyden-Fletcher-Goldfarb-Shanno updating formula. A training set of 80 DNA sequences is chosen from the HMR195 database. The objective function for the optimization procedure is formulated using the so-called receiver operating characteristic (ROC) technique and the procedure is initialized using electron-ion interaction potential (EIIP) values. Unbiased testing of the optimized characteristic values is carried out using a set of DNA sequences that has no overlap with the training set. Simulation results show that the pseudo-EIIP values yield more accurate exon locations than those obtained using the actual EIIP values.
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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