Solving the Whistler-Blackcomb Mega Day Challenge
Bibliographic record
Abstract
The Whistler-Blackcomb (WB) Mega Day challenge requires a skier to ride all 24 lifts at the resort in a single day. Among over two million people who ski annually at WB, only 313 completed the challenge in the 14 months following the introduction of a system that tracks lift use by skier. Apart from the physical challenge of skiing, a key difficulty is finding a route that matches one’s skill level while accounting for variable lift opening and closing times. We use data from WB’s radio-frequency identification (RFID) ticketing system to estimate ski times between lifts for skiers of various skill levels. We then formulate and solve the problem by a combined, iterative integer programming and heuristic approach, up to the highest feasible skier skill level. The problem’s distinctive features preclude the use of known solution methods for similar problems; therefore, we use a practical, staged-solution approach. Our results include a recommended route that enables the greatest number of skiers, roughly the fastest quartile, to achieve the challenge. We also provide a benchmark that skiers who can ski a particular common run in 12 minutes or less should be able to complete the challenge. In the three months following communication of our recommended solution, the rate at which skiers completed the Mega Day challenge increased by two-thirds over the previous seven skiing months.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".