MétaCan
Menu
Back to cohort
Record W2042914194 · doi:10.1080/10095020.2012.708150

Abstraction of informed virtual geographic environments

2012· article· en· W2042914194 on OpenAlex

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

VenueGeo-spatial Information Science · 2012
Typearticle
Languageen
FieldComputer Science
TopicData Management and Algorithms
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsComputer scienceAbstractionSituatedGeographic information systemRepresentation (politics)GraphTheoretical computer scienceHuman–computer interactionData scienceArtificial intelligenceGeographyCartography

Abstract

fetched live from OpenAlex

We propose a novel method for the automated generation of virtual geographic environments that allows using geographic information system data to build what we call informed virtual geographic environment (IVGE). The description of an IVGE integrates semantic information expressed using conceptual graphs, a standard knowledge representation technique. In addition, we propose an abstraction process that uses geometric, topologic, and semantic characteristics of geographic features to build a hierarchical graph-based structure describing this IVGE. Our IVGE model enables the support of large-scale and complex geographic environment modeling for multiagent geo-simulations in which the agents are situated and with which they interact.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.030
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 head0.013
GPT teacher head0.243
Teacher spread0.230 · 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