Mountain Ski Maps of North America: Preliminary Survey and Analysis of Style
Bibliographic record
Abstract
This article examines mountain ski resort trail maps in North America in 2008. It looks at the styles of maps used by resorts and at the main artists involved in producing the maps. The survey included maps from 428 resorts with additional analysis of maps from the 100 largest resorts. Point of view and creation method are the primary factors in determining the style of each ski trail map. Artists have employed three main types of views for ski mountains: panoramas, profiles, and planimetric maps. Panoramic views are by far the most common type of map (86% of all maps and all of the maps at the top 100 areas). Profile views are used in 8% of the maps and planimetric views in only 6%. Production methods for ski trail maps fall into three main categories: painting, illustrating, and computer rendering. Maps created with painting techniques are the most widespread, in use at 72% of all resorts and at 89% of the top 100 areas. Those created in a hard-edged vector-based illustration style are in use at 20% of resorts and those created through computer modeling and rendering at 3% of resorts.Many artists have created ski trail maps for resorts in North America but one artist, James Niehues, has produced by far the most maps in current use. His maps are in use at over a quarter of all ski areas and at half of the top resorts. Niehues follows in the footsteps of two other Coloradans, Hal Shelton and then Bill Brown, and this Colorado School has been key in the development of a classic painted panoramic style of North American ski maps. Additional research is recommended to provide further details of the history of the maps and their creators as well as to analyze the artists’ terrain manipulations and to look at the growing use of electronic trail maps.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".