Bibliographic record
Abstract
Strong gusty wind events were responsible for some of the poor performances of competitors and resulted in schedule changes during the PyeongChang 2018 Olympic and Paralympic Winter Games. Three events at two venues were investigated to document and articulate the wind forecasting and nowcasting challenges. Upper air analysis showed that the Games were dominated by northwesterly synoptic flow. Froude and Reynolds number analyses indicated that vortex shedding or wake turbulence were the dominant mechanisms in the lee of the mountains where the free-style competitions were conducted. Three types of wind data (10 and 1 min averages plus 1 minute maximums) from automatic weather stations that were reported every minute were analyzed using advanced techniques (Hovmueller, wavelet and eigen analysis frequency estimation). For the two days of Event 1, the conditions were well mixed throughout the day and night. For the other events, diurnal variations were observed with a stable atmosphere at night, well mixed in the afternoon and with 2–4 hour transition periods in the morning and evenings. Turbulence was best portrayed using wavelet analysis and vortex shedding was best portrayed using the eigen analysis frequency estimation method. The latter revealed dominant frequencies, presumably associated with vortex shedding with periodicities of 20 to 90 minutes. Nowcast implications are discussed.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.035 | 0.018 |
| Insufficient payload (model declined to judge) | 0.045 | 0.048 |
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".