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Record W2039536352 · doi:10.1080/02640410802027360

Ice friction of flared ice hockey skate blades

2008· article· en· W2039536352 on OpenAlex
Peter Federolf, Robert E. Mills, Benno M. Nigg

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Sports Sciences · 2008
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsIce hockeySkateGeologyEngineeringPhysical medicine and rehabilitationStructural engineeringMedicine

Abstract

fetched live from OpenAlex

In ice hockey, skating performance depends on the skill and physical conditioning of the players and on the characteristics of their equipment. CT Edge have recently designed a new skate blade that angles outward near the bottom of the blade. The objective of this study was to compare the frictional characteristics of three CT Edge blades (with blade angles of 4 degrees, 60, and 8 degrees, respectively) with the frictional characteristics of a standard skate blade. The friction coefficients of the blades were determined by measuring the deceleration of an aluminium test sled equipped with three test blades. The measurements were conducted with an initial sled speed of 1.8 m s(-1) and with a load of 53 kg on each blade. The friction coefficient of the standard blades was 0.0071 (s = 0.0005). For the CT Edge blades with blade angles of 4 degrees, 6 degrees, and 8 degrees, friction coefficients were lower by about 13%, 21%, and 22%, respectively. Furthermore, the friction coefficients decreased with increasing load. The results of this study show that widely accepted paradigms such as "thinner blades cause less friction" need to be revisited. New blade designs might also be able to reduce friction in speed skating, figure skating, bobsledding, and luge.

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.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.025
GPT teacher head0.280
Teacher spread0.255 · 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