Bibliographic record
Abstract
The workflow's name has been changed from nf-iav-illumina to nf-flu and the official repo for nf-flu will be CFIA-NCFAD/nf-flu going forward. Version 3 is a major release adding a Nanopore influenza sequence analysis subworkflow using IRMA for initial assembly and BLAST against NCBI Influenza DB sequences and optionally, user-specified sequences to identify the top reference sequence for each segment for each sample. A standard read mapping/variant calling analysis is performed: for each sample, Nanopore reads are mapped separately against each gene segment reference sequence using Minimap2; variant calling of read alignments is performed using Clair3; depth-masked consensus sequence is generated using Bcftools. Consensus sequences are BLAST searched against NCBI Influenza (and user-specified sequences) to generate a BLAST summary report and H/N subtyping report. MultiQC is used to summarize results into an interactive HTML report. NOTE: Read mapping/variant calling analysis has not been ported to the Illumina sequence analysis subworkflow. 3.1.0 changes Added back bin/fastq_dir_to_samplesheet.py for Illumina --input samplesheet creation from Illumina FASTQ reads directory Fixed issue #12. Nanopore sample sheet can specify a mix of single FASTQ files and/or directories containing FASTQ files. Different reads with the same sample name will be merged prior to analysis. FASTQs can be GZIP compressed and have the extensions: .fastq, .fq, .fastq.gz, .fq.gz. Updated CI tests to test for this flexible sample sheet handling. Switched to GitHub YAML form for bug report template from Markdown template. CI tests now output results/pipeline_info/ and .nextflow.log as artifacts for easier debugging of issues.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.101 | 0.156 |
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".