Depositing annotated sequences in GenBank: there needs to be a better way
Bibliographic record
Abstract
Submitting sequences to the National Center for Biotechnology Information (NCBI) is an integral part of research and the publication process for many disciplines within the life sciences, and it will only become more important as sequencing technologies continue to improve. Here, I argue that the available infrastructure and resources for uploading data to NCBI-especially the associated annotations of eukaryotic genomes-are inefficient, hard to use and sometimes just plain bad. This, in turn, is causing some researchers to forgo annotations entirely in their submissions. The time is overdue for the development of sophisticated, user-friendly software for depositing annotated sequences in GenBank.
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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 | 0.047 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.045 | 0.053 |
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".