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Record W7117982698 · doi:10.1007/979-8-8688-2148-6_6

Texture

2025· book-chapter· en· W7117982698 on OpenAlexaff
James Parker

Bibliographic record

VenueApress eBooks · 2025
Typebook-chapter
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsRockyview General Hospital
Fundersnot available
KeywordsTexture (cosmology)Rendering (computer graphics)Texture filteringShadingVisualizationImage texture

Abstract

fetched live from OpenAlex

Shading and texture are used by the human visual system as ways to determine the shape of three-dimensional objects, so their use in accurately rendering objects like trees is critical. Shading is the use of color or intensity variations across an area being drawn to illustrate shape. In particular, it is used to simulate the effect of illumination on the object. Texture amounts to the surface detail of a real object. It is a difficult concept to convey simply, and yet most people understand texture implicitly. Texture is a pattern that appears on the surface of an object. It’s probably tactile but is also visual and identifies the nature of the material. Wood, cloth, metal, and stone have tactile (touch) and visual texture, and they help humans identify objects and their shapes and orientations. As a result, when we draw objects, we need to apply what we know about textures to the rendering process.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.662
Threshold uncertainty score1.000

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 science0.0010.001
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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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