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Record W2122803926 · doi:10.1145/2669485.2669505

Multi Surface Interactions with Geospatial Data

2014· article· en· W2122803926 on OpenAlex
Zahra Shakeri Hossein Abad, Craig Anslow, Frank Maurer

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 institutionsUniversity of Calgary
Fundersnot available
KeywordsGeospatial analysisComputer scienceData scienceGeographic information systemData miningGeographyRemote sensing

Abstract

fetched live from OpenAlex

Even though Multi Surface Environments (MSE) and how to perform interactions in these environments have received much attention during recent years, interaction with geospatial data in these environments is still limited, and there are many design and interaction issues that need to be addressed. Alongside the rapid rise in the use of Geographic Information Systems (GIS) in group-based decision making, interaction with geospatial data has become highly important. In order to summarize the earlier research in this area, this paper presents a systematic review of MSE interactions with geospatial data; analyzing the existing studies on MSE interaction techniques, discussing issues related to interaction with geospatial data in MSEs and providing a comparison between common GIS tasks and existing interaction techniques in MSEs. Our results indicate that a substantial number of GIS tasks have not been investigated in MSEs.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score0.541

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 science0.0010.000
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 head0.038
GPT teacher head0.294
Teacher spread0.257 · 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

Citations24
Published2014
Admission routes1
Has abstractyes

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