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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 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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.032
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0040.011
Scholarly communication0.0080.007
Open science0.0010.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0320.013

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 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
GenreOther

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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