Bibliographic record
Abstract
PURPOSE: This document provides detailed instructions on how to submit raw sequence data and associated contextual data for pathogens to NCBI while adhering to the INSDC standard data structure, "Pathogen DOM,". The protocol includes essential steps to create a new NCBI submission environment for your laboratory group, which is crucial to have in place before data are submitted. After these initial setups, the the remaining protocol focuses on step-by-step instructions for data submission. GUIDANCE FOR NEW SUBMITTERS: Before initiating your first data submission, there is significant preparatory work required. We advise designating a team member to spend several days setting up the necessary systems well before your anticipated first submission. Watch NCBI's 10min video tutorial describing general submission to SRA. ADVICE FOR FREQUENT/LARGE VOLUME SUBMISSIONS: Start by following Step 1 to establish your NCBI submission environment. For ongoing or large-scale submissions, email gb-admin@ncbi.nlm.nih.gov to arrange an account for API-based submissions. CDC maintains the following API-based submission tool: TOASTADAS Version history:
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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.542 | 0.571 |
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".