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Record W3196635699 · doi:10.48550/arxiv.2108.12635

An Analysis and Critique of the Scoring Method Used for Sport Climbing at the 2020 Tokyo Olympics

2021· preprint· en· W3196635699 on OpenAlexaff
Michela J. Stinson, Douglas R. Stinson

Bibliographic record

VenuearXiv (Cornell University) · 2021
Typepreprint
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsProduct (mathematics)ClimbingRanking (information retrieval)Event (particle physics)Square (algebra)Computer scienceMathematicsStatisticsArtificial intelligenceOperations researchEngineering

Abstract

fetched live from OpenAlex

Sport climbing was a new Olympic event introduced at the Tokyo 2020 Olympics. It was composed of three disciplines, and the final rankings were determined by computing the product of each climber's rankings in the three disciplines, with the lowest score winning. In this paper, we compare this product-based scoring method with the more usual sum-based method. As well, we propose and analyze a new method based on taking the sum of the square roots of each climber's rankings.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.048
metaresearch head score (Gemma)0.175
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.255

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.175
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0020.007
Scholarly communication0.0050.003
Open science0.0040.002
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0020.002

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 head0.066
GPT teacher head0.281
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2021
Admission routes1
Has abstractyes

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