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Record W1967408316 · doi:10.3138/9862-21ju-4021-72m3

Planetary Maps: Visualization and Nomenclature

2006· article· en· W1967408316 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Geography and Cartography
Canadian institutionsnot available
FundersMagyar Tudományos Akadémia
KeywordsVisualizationNomenclaturePlanetary scienceAtlas (anatomy)Computer scienceGeographyCartographyTaxonomy (biology)AstrobiologyArtificial intelligenceGeologyPhysicsBiologyEcology

Abstract

fetched live from OpenAlex

Planetary maps are powerful tools for the visualization of the formerly unknown planetary surfaces. The appropriate use of visualization and nomenclature is essential for making planetary maps that can be used by both professionals and the general public. This article describes an international mapping project that has produced several maps of the terrestrial planets and the Moon. The maps were published separately, as educational wall maps, and also appeared together in a world atlas. To select the most effective visual tools and nomenclature, we conducted a map reader perception study at the Eötvös Loránd University, Hungary, which is discussed in detail. The second part of the article describes the current system of planetary nomenclature, highlighting some of its problems, with special attention to its localization for bi- or multilingual planetary maps.

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.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
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 head0.006
GPT teacher head0.266
Teacher spread0.260 · 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