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Record W2983403820 · doi:10.1007/978-981-32-9915-3_2

Digital Earth Platforms

2019· book-chapter· en· W2983403820 on OpenAlexaff
Troy Alderson, Matthew B.J. Purss, Xiaoping Du, Ali Mahdavi‐Amiri, Faramarz Samavati

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
Topic3D Modeling in Geospatial Applications
Canadian institutionsSimon Fraser UniversityUniversity of Calgary
Fundersnot available
KeywordsRepresentation (politics)Computer scienceSearch engine indexingProjection (relational algebra)Focus (optics)Selection (genetic algorithm)Digital EarthTheoretical computer scienceInformation retrievalArtificial intelligenceGeographyRemote sensingAlgorithmPolitical sciencePhysics

Abstract

fetched live from OpenAlex

Abstract In this chapter, we provide a thorough discussion on Digital Earth with particular focus on Discrete Global Grid Systems (DGGS), which are a standardized representation of the Earth. We describe the necessary components of a DGGS, such as the underlying 2D representation, indexing system, projection, and cell types. We also discuss a selection of well-known public and commercial DGGSs followed by current DGGS standards.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.179
Threshold uncertainty score0.598

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0030.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1790.103

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 head0.011
GPT teacher head0.180
Teacher spread0.169 · 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

Citations29
Published2019
Admission routes1
Has abstractyes

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