PROBABILISTIC MODELING AND ANALYSIS OF DNA FRAGMENTATION
Bibliographic record
Abstract
Deoxyribonucleic Acid (DNA) sequencing has become indispensable to the modern biological and medicine sciences. DNA fragmentation is a preliminary step in a dominant technique called shotgun sequencing that provides a time and cost effective strategy for the DNA sequencing. In this paper, we propose a probabilistic model for the random DNA fragmentation and derive an average number of fragments with the suitable length along with the probability of covering the entire DNA strand through the de novo assembly or the referenced-based mapping assembly. We formulate the coverage problem in terms of the probability of bond breaking between nucleotides and the number of DNA molecules participating in the fragmentation process, and provide insights into the optimal DNA fragmentation. We obtain the lower bound for the minimum number of suitable fragments required to reconstruct the DNA strand with the specified reliability. We evaluate the derived results with our DNA Fragmentation Tool which demonstrate, the validity of these results based on our model. Finally, we update our model with respect to the fragments’ size distribution of real data.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".