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Record W2071148888 · doi:10.1145/2793107.2810266

Alaska Steve

2015· article· en· W2071148888 on OpenAlexaff
Liam Collins, Stefan Eylott, John Leedale, T.C. Nicholas Graham

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsQueen's University
Fundersnot available
KeywordsVirtual realityMetagamingComputer scienceImmersion (mathematics)Context (archaeology)Game art designHuman–computer interactionVideo game designMixed realityGame DeveloperPerspective (graphical)Character (mathematics)Game designMultimediaArtificial intelligenceNon-cooperative gameSimultaneous gameGame theoryGeographyMathematics

Abstract

fetched live from OpenAlex

Traditional virtual reality games foster immersion by giving the player a first-person perspective of a three-dimensional world. Alaska Steve: Bite Frost Back takes a different approach by using the Oculus Rift's orientation tracking as a core game mechanic for 2D platforming. The use of virtual reality allows the player to maintain context while navigating and manipulating the large two-dimensional game world as the main character travels through it. The game's obstacles complement the use of virtual reality by requiring the player to quickly scan the game world independently of the main character.

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.999

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)0.0000.001

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 head0.035
GPT teacher head0.273
Teacher spread0.237 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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