ktmeaton/ncov-recombinant: v0.7.0 - Recursive Recombinants
Bibliographic record
Abstract
Notes This is a minor release aimed towards a nextclade dataset upgrade from 2022-10-27 to 2023-01-09 which adds nomenclature for newly designated recombinants XBH - XBP. This release also adds initial support for the detection of "recursive recombination" including XBL and XBN which are recombinants of XBB. A comprehensive test summary report can be downloaded directly with: ncov-recombinant_v0.6.1_v0.7.0.zip or viewed at the following link once the release is complete. Documentation Issue #24: Create documentation on Read The Docs Dataset Issue #210: Handle numeric strain names. Resources Issue #185: Simplify creation of the pango-lineage nomenclature phylogeny to use the lineage_notes.txt file and the pango_aliasor library. sc2rf Issue #195: Add bypass to intermission allele ratio for edge cases. Issue #204: Add special handling for XBB sequenced with ARTIC v4.1 and dropout regions. Issue #205: Add new column parents_conflict to indicate whether the reported lineages from covSPECTRUM conflict with the reported parental clades from `sc2rf. Issue #213: Add XBK to auto-pass lineages. Issue #222: Add new parameter --gisaid-access-key to sc2rf and sc2rf_recombinants. Issue #229: Fix bug where auto-pass lineages are missing when exclude_negatives is set to true. Issue #231: Fix bug where 'null' lineages in covSPECTRUM caused error in sc2rf postprocess. The order of the postprocessing.py was rearranged to have more comprehensive details for auto-pass lineages. Add XAN to auto-pass lineages. Plot Issue #209: Restrict the palette for rbd_level to the range of 0:12. Issue #218: Fix bug concerning data fragmentation with large numbers of sequences. Issue #221: Remove parameter --singletons in favor of --min-cluster-size to control cluster size in plots. Issue #224: Fix bug where plot crashed with extremely large datasets. Combine plot and plot_historical into one snakemake rule. Also at custom pattern plot_NX (ex. plot_N10) to adjust min cluster size. Report Combine report and report_historical into one snakemake rule. Validate Issue #225: Fix bug where false negatives passed validation because the status column wasn't checked. Designated Lineages Issue #217: XBB.1.5 Issue #196: XBF Issue #206: XBG Issue #196: XBH Issue #199: XBJ Issue #213: XBK Issue #219: XBL Issue #215: XBM Issue #197: XBN Proposed Lineages Issue #203: proposed1305 Issue #208: proposed1340 Issue #212: proposed1425 Issue #214: proposed1440 Issue #216: proposed1444 Issue #220: proposed1576 Commits c279f1e4 docs: add changelog for v0.7.0 2964b4a1 docs: update notes to include 1576 proposed issue fdc874ab docs: add test summary package for v0.7.0 3f3d4438 docs: update docs v0.7.0 78696b36 script: add bug fix to sc2rf postprocess for #231 403777a0 script: lint plotting script 2a09c783 script: fix sc2rf postprocess bug in duplicate removal d44d5f90 data: add XBP to controls-gisaid 4293439c profile: add controls-gisaid to virusseq builds 91d6fb89 defaults: update nextclade dataset to 2023-02-01 630b2cd5 resources: update 49e6f598 profile: add virusseq profile 7e586d1d script: add extra logic for auto-passing lineages 0ebe5e9c script: fix bug in report where it didn't check that plots existed 25b2f243 docs: update developers guide 914d933f defaults: add XBN to controls-gisaid and validation 8eaf08a9 data: restore controls-gisaid strain list fa123009 script: defragment plot for 218 5f24f695 dataset: update controls-gisaid strain list efc5aab7 defaults: update validation to fix XBH dropout See CHANGELOG.md for additional commits.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.246 | 0.305 |
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".