The COVID-19 athlete passport: a tool for managing athlete COVID-19 status surrounding the Tokyo 2020 Olympic games
Bibliographic record
Abstract
COVID-19 has strongly impacted sporting participation at all levels of competition, with many large-scale events postponed or even cancelled. Mass gatherings at sporting events have also been severely restricted;(1) such gatherings are a known source of infectious disease transmission, with the potential for global spread upon return to home country.(2) Moreover, it has been previously reported from the 2018 Winter Olympics that infectious diseases, in particular respiratory tract infections, may spread readily within the same sporting discipline or team.(2) This presents strong rationale to manage the participating athletes appropriately to prevent further outbreak of COVID-19. <br/><br/>The postponed Tokyo 2020 Olympic Games were supposed to be the most attended gathering in sport of 2020, with the expected participation of 11,090 Olympic athletes and 4400 Paralympic athletes.(3) Now rescheduled to begin in July 2021 (but with potential for further deferral), it is paramount that the COVID-19 status of athletes is managed appropriately to ensure smooth sporting participation and to avoid unnecessary impedance of participants during their training, travel and stay.<br/>
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".