Climate change and the climate reliability of hosts in the second century of the Winter Olympic Games
Bibliographic record
Abstract
In the year the world first surpassed the 1.5°C dangerous global warming threshold set out in the Paris Climate Agreement, the international community celebrates the 100th anniversary of the Olympic Winter Games and considers its future in a warmer world. In the largest study to date, the climate reliability of 93 locations to host Olympic and Paralympic Winter Games (OWG and PWG) snow sports is examined. Under a more probable mid-range emission scenario (RCP4.5, SSP2-45), 52 locations remain climate-reliable for the OWG in the 2050s and 46 in the 2080s. The scheduling of the PWG in March put it at higher risk, with only 22 climate-reliable locations in the 2050s and 16 in the 2080s. When a more stringent minimum snow depth requirement was applied, the number of reliable locations declined slightly for both OWG and PWG, signifying the importance of advanced sustainable snowmaking as an adaptation strategy. While it is inevitable that climate change will impact the geography and development of winter sports to some degree, a reassuring finding is that even with a diminished pool of potential host locations, with continued adaptation, the OWG-PWG can endure as a genuinely global celebration of sport.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".