MétaCan
Menu
Back to cohort
Record W2393610540

Interpretation of GIS Development Trend Based on Literature Statistics

2015· article· en· W2393610540 on OpenAlexaboutno aff
MA Yuron

Bibliographic record

VenueGeospatial Information · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEvaluation Methods in Various Fields
Canadian institutionsnot available
Fundersnot available
KeywordsChinaGeographyRegional scienceDistribution (mathematics)Geographic information systemInterpretation (philosophy)CartographyPhysical geographyComputer scienceArchaeologyMathematics
DOInot available

Abstract

fetched live from OpenAlex

The paper studied on characteristics of GIS in bibliometric by International Journal of Geographic Information Science and Computers Geosciences for 2001~2010 years to find the gap between China and the United States,Canada and other Western countries.And then,this paper sumed up authors,institutions distribution characteristics of papers,and tried to understand and analyze GIS technology developments and trends at home and abroad for 10 years,in order to provide reference and basis for the development of GIS and 3S.

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)0.005
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0430.061
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.290
Teacher spread0.269 · 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.

Study designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueGeospatial InformationSame topicEvaluation Methods in Various FieldsFrench-language works237,207