MétaCan
Menu
Back to cohort
Record W2004166955 · doi:10.1145/2030441.2030454

Double meandering algorithm

2011· article· en· W2004166955 on OpenAlexafffund
Shelley Gao, Lucy Pullen, Amy A. Gooch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Victoria
KeywordsAnimationComputer scienceComputer graphics (images)AlgorithmProcess (computing)Point (geometry)Line (geometry)Presentation (obstetrics)Computer animationLine drawingsLine segmentComputer visionEngineering drawingMathematicsGeometryEngineering

Abstract

fetched live from OpenAlex

We introduce artist Lucy Pullen's Double Meandering Algorithm, first in its original form as a pen-and-paper drawing algorithm and then as a procedurally generated animation. We utilize a chain of cubic Bézier curves to represent the characteristic spiraling line, assigning each control point according to a pseudo-randomized algorithm. The resulting curves are then animated segment by segment, reflecting the artist's process of creating the pen-and-paper drawing. By digitizing the Double Meandering Line drawing, we can also reveal the process of creation through animation, granting us the ability to exhibit a fundamental part of the drawing that is lost in the traditional pen-and-paper presentation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.777
Threshold uncertainty score0.193

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)0.0000.000

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 head0.062
GPT teacher head0.281
Teacher spread0.219 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2011
Admission routes2
Has abstractyes

Explore more

Same topicComputer Graphics and Visualization TechniquesFrench-language works237,207