Authors' Response to Peer Reviews of “COVID-19 Outcomes and Genomic Characterization of SARS-CoV-2 Isolated From Veterans in New England States: Retrospective Analysis”
Bibliographic record
Abstract
Genomic Characterization of SARS-CoV-2 Isolated From Veterans in New England States: Retrospective Analysis."Lee et al JMIRx Med XSL • FORenderX from the United Kingdom, B.1.1.28from Brazil, and B.1.351from South Africa [13] warrant constant new data and knowledge translation.To this effect, this paper addresses a major area of concern and interest to the readership of the journal.The authors are clear in their title, which still needs to fully comply with the journal guidelines.The Abstract follows the guidelines and presents an overview of the study.Being an area that has received tremendous interest since the start of the COVID-19 pandemic, there was an overriding need for this study to be put in context.The paper's introduction does well, ends with the study aim, and is brief at highlighting the main concern but deserves more attention.The general structure of the paper needs improvement to comply with the journal guidelines.The data collection methods, albeit needing clarification, seem reasonable with appropriate analysis, thereby giving value to the results.The discussion of the paper has been well articulated, and the conclusion ties with the research objective.The English used is simple and in plain language for easy comprehension.Although congratulating the authors for a good attempt and concise paper, the paper will benefit from more value if the following specific comments are given consideration.
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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 | 0.024 | 0.286 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.078 | 0.044 |
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".