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Record W4366975608 · doi:10.1111/tgis.13054

Enabling geosimulations for global scale: Spherical geographic automata

2023· article· en· W4366975608 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTransactions in GIS · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarth Systems and Cosmic Evolution
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCellular automatonScale (ratio)Earth system sciencePopulationComputer scienceDeforestation (computer science)CurvatureGeographyCartographyGeologyMathematicsAlgorithmGeometry

Abstract

fetched live from OpenAlex

Abstract Several complex dynamic spatial systems are operating on the global scale. Their representation with existing geosimulation models is limited to planar level and do not consider the curvature of the Earth's surface. Thus, the objective of this study is to propose and develop a spherical geographic automata (SGA) modeling approach to represent and simulate dynamic spatial processes at the global level. The proposed SGA model is implemented for three case studies including simulations of: (1) Game of Life as population dynamics; (2) urban land‐use growth; and (3) deforestation all operating on the spherical Earth's surface. Simulation results indicate that the proposed SGA modeling approach can represent spatial processes such as expansion and shrinkage dynamics on the Earth's surface. The proposed approach has the potential to be adopted to represent different complex systems such as ecological, epidemiological, socioeconomic, and Earth systems processes to support environmental management and policymaking at the global level.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.257
Teacher spread0.235 · 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