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Record W1963843280 · doi:10.1080/13658810802634956

An overview on current free and open source desktop GIS developments

2009· article· en· W1963843280 on OpenAlexaff
Stefan Steiniger, Erwan Bocher

Bibliographic record

VenueInternational Journal of Geographical Information Systems · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsUniversity of Calgary
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsLicenseGeospatial analysisOpen source softwareSoftwareComputer scienceGeographic information systemSoftware engineeringOpen sourceWorld Wide WebData scienceGeographyCartography

Abstract

fetched live from OpenAlex

Over the past few years the world of free and open source geospatial software has experienced some major changes. For instance, the website FreeGIS.org currently lists 330 GIS‐related projects. Besides the advent of new software projects and the growth of established projects, a new organisation known as the OSGeo Foundation has been established to offer a point of contact. This paper will give an overview on existing free and open source desktop GIS projects. To further the understanding of the open source software development, we give a brief explanation of associated terms and introduce the two most established software license types: the General Public License (GPL) and the Lesser General Public License (LGPL). After laying out the organisational structures, we describe the different desktop GIS software projects in terms of their main characteristics. Two main tables summarise information on the projects and functionality of the currently available software versions. Finally, the advantages and disadvantages of open source software, with an emphasis on research and teaching, are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.032
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.020
Science and technology studies0.0010.001
Scholarly communication0.0030.007
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0320.013

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 head0.056
GPT teacher head0.379
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations253
Published2009
Admission routes1
Has abstractyes

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