Insightful skiing: developing explainable models of on-snow performance through physical attribute selection of alpine skis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it