GCRSR Proficiency Test, 2021
Bibliographic record
Abstract
The application of whole-genome sequence (WGS) technology in regulatory food microbiology provides an unprecedented opportunity to produce highly informative laboratory analyses supporting risk assessment and risk management actions. The quality of WGS datasets will have a significant impact on downstream bioinformatics processes, with one critical element being the possible presence of adventitious DNA sequences due to contamination during sample handling and sequencing operations. This dataset is part of a project aimed to address the need to assure WGS data quality by accounting for contamination events through determination of the impacts of sequencing data contamination events on downstream analyses, such as typing and marker discovery. The goal is to contribute to the development and implementation of harmonized quality protocols for the application of WGS technologies in the international regulatory food microbiology community. This record consists of three in silico datasets that will be used for proficiency tests. They are organized into mock Illumina MiSeq sequencing runs. The runs consist of the same 24 Escherichia coli samples. FASTQ files for these samples were created by simulating reads from MinION/PacBIO + Illumina MiSeq hybrid-assembly polished genomes or closed reference genomes downloaded from NCBI. Reads were simulated with ART. One run was created using the empirical Illumina MiSeq profile, while the other runs were simulated using custom read profiles generated from over-clustered runs. The reference strains represent a diverse cross-section of serotypes, shiga toxin subtype, antimicrobial resistance (AMR) profiles, plasmid profiles. Please read the "Protocol_GCRSR_Proficiency_Test_2021.pdf" file for instructions and further information.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.033 |
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".