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Record W2035524896 · doi:10.1068/p6876

Drawing with Divergent Perspective, Ancient and Modern

2011· article· en· W2035524896 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePerception · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsYork University
Fundersnot available
KeywordsCube (algebra)Perspective (graphical)Set (abstract data type)Subject (documents)Order (exchange)Computer scienceVisual artsArtMathematicsGeometry

Abstract

fetched live from OpenAlex

Before methods for drawing accurately in perspective were developed in the 15th century, many artists drew with divergent perspective. But we found that many university students draw with divergent perspective rather than with the correct convergent perspective. These experiments were designed to reveal why people tend to draw with divergent perspective. University students drew a cube and isolated edges and surfaces of a cube. Their drawings were very inaccurate. About half the students drew with divergent perspective like artists before the 15th century. Students selected a cube from a set of tapered boxes with great accuracy and were reasonably accurate in selecting the correct drawing of a cube from a set of tapered drawings. Each subject's drawing was much worse than the drawing selected as accurate. An analysis of errors in drawings of a cube and of isolated edges and surfaces of a cube revealed several factors that predispose people to draw in divergent perspective. The way these factors intrude depends on the order in which the edges of the cube are drawn.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.208
Teacher spread0.171 · 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