A brief comparison of DSP and HMM methods for gene finding
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
In this paper, we briefly compare the performance of gene finding methods which are based on hidden Markov models (HMMs) and digital signal processing (DSP). We apply the methods to three benchmark datasets consisting of sequences from various species (mammalian, vertebrate and non-vertebrate) to investigate the strength and weakness of these two classes of methods from different aspects. We study the effect of training on the HMM-based methods. In addition, we analyze the effect of the threshold and window length parameters on the performance of the DSP-based methods. In our work, we present the receiver operating characteristic (ROC) plots of the DSP-based methods and the numerical results of applying the HMM and DSP-based methods to the three datasets. In addition, the plots of the prediction accuracy of the DSP-based methods when they are applied to exons of specific lengths and with different values of the window length parameter are presented in this study.
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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