Challenges and Opportunities of Genomic Surveillance SARS-CoV-2 in Mexico Meeting
Bibliographic record
Abstract
In late 2019, a new virus, SARS-CoV-2, emerged in Wuhan, China, causing COVID-19 and the subsequent global pandemic. As of 30 April 2023, more than 774 million cases of COVID-19 had been reported worldwide, including over 7.5 million in Mexico. Despite advances in vaccination, epidemic surges of COVID-19 continued to occur globally, highlighting the importance of sharing and disseminating the experiences gained during these first years to better understand the virus’s evolution and respond accordingly. For this reason, the National Council for Science and Technology (CONACYT) organized the meeting “Challenges and Opportunities for Genomic Surveillance of SARS-CoV-2 in Mexico” from 15 to 17 August 2022, to present the efforts and results accumulated over more than two years of the pandemic. In this meeting report, we summarize the key findings of each participant and provide their contact information.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".