Bibliographic record
Abstract
Please cite: Wei Shen*, Botond Sipos, and Liuyang Zhao. 2024. SeqKit2: A Swiss Army Knife for Sequence and Alignment Processing. iMeta e191. doi:10.1002/imt2.191. Wei Shen, Shuai Le, Yan Li*, and Fuquan Hu*. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLOS ONE. doi:10.1371/journal.pone.0163962. Changes SeqKit v2.8.2 - 2024-05-17 seqkit amplicon: Fix a big introduced in v2.7.0. When more than one pairs of primers are given, only the last one is used. #457 seqkit translate: Add option -e/--skip-translate-errors to skip translate error and output empty sequence. #458 seqkit split: Add flag -I/--ignore-case for -i/--by-id. #462 Links OS |Arch |File, 中国镜像 |Download Count :------|:---------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Linux |32-bit |seqkit_linux_386.tar.gz, 中国镜像 | Linux |64-bit|seqkit_linux_amd64.tar.gz, 中国镜像 | Linux |arm64 |seqkit_linux_arm64.tar.gz, 中国镜像 | macOS |64-bit|seqkit_darwin_amd64.tar.gz, 中国镜像 | macOS |arm64 |seqkit_darwin_arm64.tar.gz, 中国镜像 | Windows|32-bit |seqkit_windows_386.exe.tar.gz, 中国镜像 | Windows|64-bit|seqkit_windows_amd64.exe.tar.gz, 中国镜像| Notes please open an issuse to request binaries for other platforms. run seqkit version to check update !!! run seqkit genautocomplete to update shell autocompletion script !!!
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.281 | 0.424 |
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".