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Record W1983283514 · doi:10.3138/carto.48.1.1228

Designing Effective Legends and Layouts with a Focus on Nigerian Topographic Maps

2013· article· en· W1983283514 on OpenAlexvenueno aff
Felicia O. Akinyemi, P.M. Kibora, P. Aborishade

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLegendWorkflowTopographic map (neuroanatomy)Focus (optics)ColonialismIndependence (probability theory)CartographyComputer scienceGeographyArchaeologyDatabaseMathematics

Abstract

fetched live from OpenAlex

Most mapping research efforts currently focus on the content of maps and the platform used. By examining layout (the spatial arrangement of various map elements) and legends (an example of marginal map information) in topographic maps, this study complements other studies on map content and use. A map's legend, sometimes known as a “key,” enables the reader to decipher the meanings of the marks and forms that make up the map's content. The layout style and legend used in many topographic map series in Africa were adopted from colonial maps – for example, Nigeria's topographic maps are reminiscent of British colonial maps – and thus post-independence topographic maps reflect the legacies of colonial mapping. This article describes the design of a layout and legend created, using a digital workflow based on the existing analogue legend, for the Nigerian 1:50,000 topographic map series. Classes of vegetation and transportation features depicted on both the old legend and the proposed new legend were compared to illustrate the enhancements achieved in the latter.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
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 score0.531
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
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 head0.009
GPT teacher head0.274
Teacher spread0.264 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations3
Published2013
Admission routes1
Has abstractyes

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