SARS-CoV-2 NCBI submission workflow + guidance for structuring and releasing metadata v1
Bibliographic record
Abstract
PURPOSE: This workflow provides an overview on the metadata specification recommend for SARS-CoV-2 sequence data and a series of protocols outlining the steps for NCBI submission. PHA4GE Contextual Metadata SOP: This protocol provides step-by-step instructions for populating the template, and also addresses a number of ethical, privacy and practical considerations that should be discussed with your data steward prior to any type of data sharing. The appendices provide additional instructions and examples of how to curate sample type descriptions, and how to identify additional standardized terms should you need them. Overview of NCBI's submission process and the metadata required: Provides an overview of the submission process and includes a brief training video. SARS-CoV-2 NCBI submission protocol: SRA, BioSample, and BioProject Step-by-step instructions for establishing a new NCBI laboratory submission account and for creating and linking a new BioProject to an existing umbrella effort. SARS-CoV-2 raw data submission to SRA (Sequence Read Archive) and metadata to BioSample. SARS-CoV-2 NCBI consensus submission protocol: GenBank Required: established BioProject and BioSamples Submit SARS-CoV-2 assemblies to NCBI GenBank, linking to existing BioProject, BioSamples, and raw data.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".