Bibliographic record
Abstract
This release adds merging of BAM/FASTQ files for the same samples prior to analysis. Added [feat] Merging of reads across multiple runs for the same sample with CAT_IONTORRENT_BAM and CAT_IONTORRENT_FASTQ. If multiple reads sets are present for a sample, BAM files are merged with samtools merge and FASTQ files are merged cat, otherwise, symlinks are made. Additional steps are taken to preserve original read group (@RG) information for all input BAM files as well as rename sample name if required, so that merged BAM files are compatible with TVC variant calling. Sample renaming may occur when running the pipeline with a samplesheet rather than relying on the sample names within the BAM files themselves. [test] nf-test for CAT_IONTORRENT_BAM and CAT_IONTORRENT_FASTQ along with minimal test data and scripts used to generate test data. Fixed [fix] samplesheet handling What's Changed Add BAM and FASTQ merging step to pipeline by @peterk87 in https://github.com/CFIA-NCFAD/nf-ionampliseq/pull/10 Release 2.3.0 by @peterk87 in https://github.com/CFIA-NCFAD/nf-ionampliseq/pull/11 Full Changelog: https://github.com/CFIA-NCFAD/nf-ionampliseq/compare/2.2.0...2.3.0
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.040 | 0.041 |
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; both teacher heads agree on what is shown here.
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".