Insightful skiing: developing explainable models of on-snow performance through physical attribute selection of alpine skis
Bibliographic record
Abstract
Evaluating alpine skis on snow is pivotal for ski development and consumer decision-making, yet it is resource-intensive and hindered by subjective assessments. Leveraging recent extensive ski physical measurements and on-snow ski evaluation metrics, this study proposes an automated methodology that employs elastic net regression, bootstrap resampling, and intelligent feature selection to predict the on-snow performance using a minimal set of physical attributes. Results on 192 skis divided into 10 categories and 29 metrics indicate promising predictive capabilities, with models exhibiting an average Mean Absolute Error rank prediction of 15%. Importantly, the models utilize less than three physical attributes on average, underscoring their simplicity and effectiveness in identifying key performance-defining properties. These findings, to the authors' knowledge, represent the most comprehensive description of ski on-snow performance to date and hold implications for ski design and consumer guidance. Moreover, the automated methodology enables the easy integration of other evaluation sources, facilitating further refinement and validation, while promising to consider the diversity of opinions related to ski on-snow performance assessment. Supplementary Information: The online version contains supplementary material available at 10.1007/s12283-025-00511-w.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".