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Record W4366987038 · doi:10.3390/app13095339

Development of Equipment for Ski Mountaineering, a New Olympic Event

2023· article· en· W4366987038 on OpenAlexaff
Lorenzo Bortolan, Barbara Pellegrini, Nina Verdel, Hans‐Christer Holmberg, Matej Supej

Bibliographic record

VenueApplied Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsUniversity of British Columbia
FundersJavna Agencija za Raziskovalno Dejavnost RS
KeywordsMountaineeringClimbingAeronauticsPopularityEngineeringGeographyPsychology

Abstract

fetched live from OpenAlex

Ski mountaineering, a new Olympic winter sport involving both climbing and descending snowy slopes, requires considerable physical and technical abilities, as well as highly specialized equipment. Herein, we briefly describe this equipment and its influence on performance and consider potential future advances. Skis, boots, and bindings must be light enough to facilitate climbing uphill (in which as much as 85% of the total racing time is spent) and, at the same time, provide stability and safety in often-challenging descents. A skier must be able to easily and rapidly attach and remove the adhesive skins under the skis that provide grip while skiing uphill. Poles and their baskets must be designed optimally to transfer propulsive force and help maintain balance. Despite the popularity of ski mountaineering, research on this sport is scarce, and we indicate a number of areas wherein improvements in equipment could potentially advance both performance and safety. Such advances must be based on a better understanding of the biomechanics of ski mountaineering, which could be obtained with novel sensor technology and can be best achieved via more extensive collaboration between researchers, skiers and their coaches, and manufacturers of ski mountaineering equipment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.325
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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