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Record W2527278991 · doi:10.1109/mcg.2016.90

Sports Tournament Predictions Using Direct Manipulation

2016· article· en· W2527278991 on OpenAlex

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

VenueIEEE Computer Graphics and Applications · 2016
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTournamentComputer scienceInterface (matter)Focus (optics)LeagueUser interfaceVisitor patternHuman–computer interactionMultimedia

Abstract

fetched live from OpenAlex

An advanced interface for sports tournament predictions uses direct manipulation to allow users to make nonlinear predictions. Unlike previous interface designs, the interface helps users focus on their prediction tasks by enabling them to first choose a winner and then fill out the rest of the bracket. In real-world tests of the proposed interface (for the 2014 FIFA World Cup tournament and 2015/2016 UEFA Champions League), the authors validated the use of direct manipulation as an alternative to widgets. Using visitor interaction logs, they were able to determine the strategies people use to perform predictions and identify potential areas of improvement for further prediction interfaces.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score0.308

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 head0.030
GPT teacher head0.286
Teacher spread0.256 · 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