Optimized numerical mapping scheme for filter-based exon location in DNA using a quasi-Newton algorithm
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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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".