Real-Time Automated Pathogen Identification by Enhanced Ribotyping (RAPIER) LDRD Final Report
Bibliographic record
Abstract
Funded through the IHNS/E&HS investment area for FY16-18, the RAPIER LDRD sought to evaluate the potential benefits and applicability of the new Oxford MinION nanopore sequencer to pathogen diagnostic applications in biodefense, biosurveillance, and global/public health. The project had four primary objectives: 1) to investigate the performance of the MinION sequencer while building facility with its operation, 2) to develop microfluidic library prep automation facilitating the use of the MinION in field-forward or point-of-care applications, 3) to leverage CRISPR/Cas9 technology to enable targeted identification of bacterial pathogens, and 4) to capitalize on the real- time data output capabilities of the MinION to enable rapid sequence-based diagnostics. While the rapid evolution of the MinION sequencing technology during the course of the project posed a number of challenges and required a reassessment of initial project priorities, it also provided unique opportunities, notably culminating in our development of the RUBRIC real-time selective sequencing software.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".