The IOC's endeavour to protect the health of the athlete continues
Bibliographic record
Abstract
After the 2010 Olympic Games in Vancouver, the International Olympic Committee (IOC) has initiated a new project together with the International Olympic Sports Federations (IFs) and the National Olympic Committees (NOC). The aim of the Health, Safety and Security (HSS) survey was to identify and, in turn, eliminate risk factors which could potentially be harmful for the Olympic athletes. Each Federation shared their data on, athletes' risk exposure, technical development and equipment evolution, venue safety procedures, rule change mechanisms and determination of athlete eligibility. The IOC and the IFs are optimistic that this survey will not only increase the awareness on injury prevention, but also facilitate the introduction of tailored measures to prevent injuries and illnesses in each sport and discipline. With this objective in mind, the IOC have during recent years developed several new programmes.1 The injury and illness surveillance system, developed in cooperation with IFs and NOCs, was implemented with success in the 2008 Beijing (injury surveillance only)2 and in the 2010 Vancouver Olympics,3 and will be further developed in the 2012 London and 2014 Sochii Olympics. Based on a consensus meeting on Periodic Health Exams (PHE),4 the IOC is developing an Electronic …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".