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Record W2611642384 · doi:10.1145/3027063.3048414

Phantasm

2017· article· en· W2611642384 on OpenAlexaff
James Bonnyman, James E.M. Conrad, Jordan M. Culver, Vincent Ho, Jacob T. Robart, Stephen Thompson, Alvan E. Tjandra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicArtificial Intelligence in Games
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsHackerInteractivityComputer scienceHuman–computer interactionInternet privacyMultimediaComputer security

Abstract

fetched live from OpenAlex

Many cooperative games share a common drawback: they are not entirely cooperative. These games usually highlight some form of personal gain, diminishing potential for positive interactivity. Phantasm is a game where players' skills are measured by how well they can communicate together. Players take on the roles of either special agent or master hacker. The goal is to get the agent to an exit. However, there are many enemies and obstacles that only the hacker can see, through various security cameras and filters. The agent, however, can hear sounds emitting from the walls and other objects. Both players must keep a constant flow of communication in aiding the agent of what routes to take, what and how to interact with the environments, and where, precisely, to shoot at invisible threats. This game will help bring players closer, as they will need to understand each others way of thinking in a tense and suspenseful gaming environment.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0020.000
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 head0.065
GPT teacher head0.343
Teacher spread0.277 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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