Environmental challenges facing athletes, stakeholders and spectators at Paris 2024 Olympic and Paralympic Games: an evidence-based review of mitigation strategies and recommendations
Bibliographic record
Abstract
The upcoming Paris 2024 Olympic and Paralympic Games could face environmental challenges related to heat, air quality and water quality. These challenges will pose potential threats to athletes and impact thousands of stakeholders and millions of spectators. Recognising the multifaceted nature of these challenges, a range of strategies will be essential for mitigating adverse effects on participants, stakeholders and spectators alike. From personalised interventions for athletes and attendees to comprehensive measures implemented by organisers, a holistic approach is crucial to address these challenges and the possible interplay of heat, air and water quality factors during the event. This evidence-based review highlights various environmental challenges anticipated at Paris 2024, offering strategies applicable to athletes, stakeholders and spectators. Additionally, it provides recommendations for Local Organising Committees and the International Olympic Committee that may be applicable to future Games. In summary, the review offers solutions for consideration by the stakeholders responsible for and affected by the anticipated environmental challenges at Paris 2024.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".