MétaCan
Menu
Back to cohort

Geographic Information Systems

2014· other· en· W2136210552 on OpenAlexaboutno aff
Daniel A. Griffith

Bibliographic record

VenueWiley StatsRef: Statistics Reference Online · 2014
Typeother
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceRaster graphicsGeographic information systemRaster dataSoftwareData miningGIS file formatSpatial analysisVisualizationVector mapAM/FM/GISGIS applicationsDigital mappingDatabaseInformation retrievalGeographyCartographyRemote sensingComputer graphics (images)Artificial intelligence

Abstract

fetched live from OpenAlex

Abstract A geographic information system (GIS) is a computer hardware and software information system designed to capture, edit, manage, house, manipulate, analyze, and display georeferenced data. It comprises high‐resolution scientific visualization capabilities, large‐capacity electronic storage devices, efficient and effective structures for data storage and retrieval, high‐volume communication channels, specialized algorithms for data integration and reliability analysis, and specialized query languages. Its reference point is a digital map. The first GIS was developed by the Canadian government and implemented in 1964. A differentiating feature among GISs is whether the underlying map involves a raster or vector surface partitioning. Spatial statistics requires attribute data, a map, and the tagging of each data observation to a location on a map, items furnished by a GIS. A GIS also supplies a practical and useful way to reveal spatial and temporal relationships among data. The UCGIS is an organization whose mission is to serve as an effective unified voice for the community of GIS users.

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)0.003
metaresearch head score (Gemma)0.017
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.245
Threshold uncertainty score0.820

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.026
Science and technology studies0.0020.001
Scholarly communication0.0110.006
Open science0.0020.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.2450.161

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 head0.027
GPT teacher head0.306
Teacher spread0.279 · 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
GenreOther

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

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueWiley StatsRef: Statistics Reference OnlineSame topicGeographic Information Systems StudiesFrench-language works237,207