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Record W2018890734 · doi:10.3138/carto.44.2.111

A Cartometric Analysis of the Terrain Models of Joachim Eugen Müller (1752–1833) Using Non-contact 3D Digitizing and Visualization Techniques

2009· article· en· W2018890734 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 · 2009
Typearticle
Languageen
FieldComputer Science
TopicImage Processing and 3D Reconstruction
Canadian institutionsnot available
FundersUniversity of BernImpact Fund
KeywordsVisualizationTerrainDigital elevation modelComputer graphics (images)Computer scienceArt historyArtCartographyGeologyArtificial intelligenceRemote sensingGeography

Abstract

fetched live from OpenAlex

This article assesses the accuracy of the terrain models of Joachim Eugen Müller (1752–1833) in relation to modern digital elevation data using non-contact 3D digitizing techniques. The results are objective testimony to the skill and endeavour of Joachim Eugen Müller. Using techniques primitive by modern standards, Müller provided Johann Henry Weiss (1758–1826) with data of hitherto unparalleled quality that were essential to the production of the Atlas Suisse par Meyer et Weiss. The results also demonstrate that non-contact 3D digitizing techniques not only provide a suitable data-capture method for solid terrain model analysis but are also a means of preserving digital facsimiles of such precious artefacts.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.012
GPT teacher head0.285
Teacher spread0.273 · 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