Establishment of SARS-CoV-2 genomic surveillance within the Military Health System during 1 March-31 December 2020.
Bibliographic record
Abstract
This report describes SARS-CoV-2 genomic surveillance conducted by the Department of Defense (DoD) Global Emerging Infections Surveillance Branch and the Next-Generation Sequencing and Bioinformatics Consortium (NGSBC) in response to the COVID-19 pandemic. Samples and sequence data were from SARS-CoV-2 infections occurring among Military Health System (MHS) beneficiaries from 1 March to 31 December 2020. There were 1,366 MHS samples sequenced from 10 countries, 36 U.S states or territories, and 5 Geographic Combatant Commands, representing approximately 2% of DoD cases in 2020. Genomes from these samples were compared with other public sequences; observed trends were similar to those of Centers for Disease Control and Prevention national surveillance in the U.S. with B.1, B.1.2, and other sub-lineages comprising the dominant variants of SARS-CoV-2. Sequence data were used to monitor transmission dynamics on U.S. Navy ships and at military training centers and installations. As new variants emerge, DoD medical and public health practitioners should maximize the use of genomic surveillance resources within DoD to inform force health protection measures.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".