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Record W2029818053 · doi:10.1145/2512349.2512792

OrMiS

2013· article· en· W2029818053 on OpenAlex
Christophe Bortolaso, Matthew Oskamp, T.C. Nicholas Graham, Doug Brown

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsKingston Health Sciences CentreQueen's University
Fundersnot available
KeywordsComputer scienceDomain (mathematical analysis)ZoomProcess (computing)Human–computer interactionPoint (geometry)Field (mathematics)Key (lock)SimplicityIterative and incremental developmentSoftware engineeringEngineering

Abstract

fetched live from OpenAlex

This paper presents the design of OrMiS, a tabletop application supporting simulation-based training. OrMiS is notable as one of the few practical tabletop applications supporting collaborative analysis, planning and interaction around digital maps. OrMiS was designed using an iterative process involving field observation and testing with domain experts. Our key design insights were that such a process is required to resolve the tension between simplicity and functionality, that information should be displayed close to the point of the user's touch, and that collaboration around maps cannot be adequately solved with a single form of zooming. OrMiS has been evaluated by domain experts and by officer candidates at a military university.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.972
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.007

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 head0.005
GPT teacher head0.206
Teacher spread0.200 · 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

Quick stats

Citations27
Published2013
Admission routes1
Has abstractyes

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