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Record W4393811358 · doi:10.5281/zenodo.10349341

Matti Nykänen's Olympic Gold Medal Calgary 1988

2021· dataset· en· W4393811358 on OpenAlexaboutno aff
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Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typedataset
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsGold medalMedalArtGeographyArchaeologyArt history

Abstract

fetched live from OpenAlex

Matti Nykänen was one of the best ski jumpers of all time. He won a total of five Olympic medals: gold and silver in Sarajevo 1984 and historically all three gold medals (normal hill, large hill and team competition) in Calgary 1988. In 1995 the Sports Museum of Finland acquired all Nykänen's major championship medals by help of a public collection campaign. On display here is the gold medal Matti Nykänen won in the large hill (90 m) competition at the Olympic Winter Games of 1988 in Calgary, Canada. The "gold" medal is actually gilded silver and has a diameter of 69 millimetres. It was designed by Friedrich Peter. The reverse side features profiles of an Olympic champion and an Indian chief and equipment used in Olympic winter sports. The event in which the medal was won is inscribed on the edge of the coin. Source: Objaverse 1.0 / Sketchfab

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.845
Threshold uncertainty score0.309

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0300.054

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 head0.043
GPT teacher head0.224
Teacher spread0.182 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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