MétaCan
Menu
Back to cohort
Record W4234654441 · doi:10.32920/ryerson.14653323

Development of an Internet-Based Synchronous GIS for Collaborative Spatial Decision Making

2021· preprint· en· W4234654441 on OpenAlexaff
Lijun Gu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceJavaThe InternetDecision support systemUrban planningGeographic information systemVisualizationSpatial decision support systemGIS and public healthData scienceKnowledge managementWorld Wide WebEngineeringGeographyData mining

Abstract

fetched live from OpenAlex

Urban and transportation development largely depends on innovative information technologies for decision making support in its planning and management processes to achieve beneficial economic, social and environmental outcomes. Among these technologies, techniques and tools for collaborative visualization, manipulation, and exploration of spatial information are particularly useful. Existing Geographic Information Systems (GISs), however, lack of the capability to support collaborative spatial decision making (CSDM). This thesis presents a research effort in the development of GIS software tools that support synchronized collaborations between multiple participants via the Internet, to explore urban and transportation development scenarios for collective decision making. While the design and development focused on integrating decision making tools with commercial GIS development Toolkits (e.g., Map Objects Java Edition) using collaborative Java APIs, the approach and insights gained should be of general interest. The initial usefulness testing indicates that an Internet-based synchronous GIS can help improve decision making processes of urban corridor planning and ease participation in such decision making activities.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.434
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.340
Teacher spread0.303 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicGeographic Information Systems StudiesFrench-language works237,207