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Record W6889174394 · doi:10.25549/examiner-c44-64122

Rugby -- Australian Wallabies, 1958

2021· dataset· en· W6889174394 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Southern California Digital Library · 2021
Typedataset
Languageen
FieldComputer Science
TopicDigital Imaging for Blood Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAmateurFootballFootball teamChampionTrade union

Abstract

fetched live from OpenAlex

5 images. Rugby -- Australian Wallabies, 24 March 1958. Des Connor; Ron Harvey; Ed Purkins; Jim Lenehan. "Sports". (Sleeve reads: SP 12289).; Caption slip reads: "Photographer: Jensen. Date: 1958-03-24. Assignment: Australian Union Rugby -- Wallabies. 94-95: Des Connor Scrum half dive passing behind Scrum. 54-55: Ron Harvey (back). 72: Ed Purkins (tackling) Jim Lenehan receiving pass".; Supplementary material reads: "From: Vic Kelley, ASUCLA News Bureau, University of California at Los Angeles. Release: Monday, March 24, 1958. The routing Wallabies, the Australian equivalent of an all-America football team, play their only Rugby match in Southern California when they take on UCLA's eager Bruins on Spaulding Field on the Westwood campus tomorrow at 3 p.m. The Wallabies have successfully toured the British Isles, France and Western Canada before coming to California for a four-match series. They will play the Bruins first and then go north to meet California, Stanford and an all-star collegiate squad. The 30 team members, picked by the Australian Rugby Union as the best amateur ruggers in the country, represent every phase of life in the 'Down Under' continent, ranging from graziers, farmers, clerks and students to engineers, lawyers, architects and business executives...".

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.480
Threshold uncertainty score0.741

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.4800.175

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 head0.007
GPT teacher head0.164
Teacher spread0.157 · 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.

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

Explore more

Same venueUniversity of Southern California Digital LibrarySame topicDigital Imaging for Blood DiseasesFrench-language works237,207