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Record W1997287837 · doi:10.3138/carto.46.3.149

Leonardo's <i>Val di Chiana</i> Map in the <i>Mona Lisa</i>

2011· article· en· W1997287837 on OpenAlex
Donato Pezzutto

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 · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicArchitecture and Art History Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDepictionStereoscopyPerspective (graphical)ArtPaintingIdentity (music)Visual artsAestheticsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Leonardo arranged the landscape in the Mona Lisa to hold two disjoined halves of one image. That image can be reassembled by juxtaposing two copies of the painting side by side. The newly reconstituted landscape corresponds to an actual place, as depicted in Leonardo's Val di Chiana map. In this article, the identity of the sitter and opinions relevant to the background landscape are considered, Leonardo's developments in the depiction of depth outlined, and his technique of topographic perspective introduced. Analysis of these observations, along with Leonardo's investigations in perception, perspective, monocular and binocular vision, and cartography, lead to understanding of his technique. Speculation as to Leonardo's motivation include a pun on La Gioconda and his attempt at stereoscopy.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.239
Teacher spread0.213 · 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