Evaluating the Accuracy of Consensus Nanosequencer Squiggles Generated by Dynamic Time Warp Barycentre Averaging (DBA)
Bibliographic record
Abstract
Picoamperage signals are generated as each nucleotide of a DNA or RNA molecule is ratcheted through a nanosequencer's nanopores by motor proteins. These are segmented into step-current level signals, "squiggles", representing the nucleotide sequence. It has been suggested that applying dynamic time warp Barycentre Averaging (DBA) to multiple noisy squiggles can generate a lower noise, less-distorted, consensus signal that retains the key squiggle characteristics that would be distorted by other averaging approaches. We discuss experimental results obtained when developing DBA consensus signals from squiggles produced by an Oxford MinION nanosequencer squiggle convertor during an Enolase study. Metrics are proposed to identify differences between the known gold standard and consensus signals, and the level of self-consistency between consensus signals developed from noisy squiggles with different length distortions. A number of location-specific differences between the gold and consensus squiggles were identified.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".