A model for return to training and competition during ongoing pandemic concerns
Bibliographic record
Abstract
In the wake of a novel Coronavirus, the sports world reeled from the realization that a pandemic of this magnitude had not been seen in more than a century. Reducing the transmission would require physical distancing to such a degree that it would necessitate the suspension of all sporting activities. The multidimensional health effects due to COVID-19 will be far more severe and prolonged if athletes cannot engage in sport at all. Most coaches are concerned with strength and conditioning maintenance as well as technical skill development in response to changes in the sport. Bringing athletes together to train while adhering to government-mandated protective measures, such as facemask use and physical distancing, proved to be a herculean task. The challenge for all sports is how to train in the setting of the new physical distancing required for a healthy community. Sail Canada ran a nine-day training camp and regatta utilizing the knowledge of a team of medical experts to adhere to the government-mandated restrictions without significantly compromising the athletic preparation. The event was a great success and could stand as a model for other sports to maintain training while still protecting the health and wellness of athletes, coaches, and officials.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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".