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Record W2990244193 · doi:10.20380/gi2019.11

Procedural Modelling with Reaction Diffusion and Growth of Thin Shells

2019· article· en· W2990244193 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

VenueCanada Human-Computer Communications Society · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Molecular Biology Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsIntuitionReaction–diffusion systemDiffusionComputer scienceBiological systemStatistical physicsMechanicsSimulationMathematicsThermodynamicsPhysicsBiologyCognitive scienceMathematical analysis

Abstract

fetched live from OpenAlex

We investigate a procedural shape modeling approach based on reaction-diffusion equations and physically based growth of thin shells. This inspiration of this work comes from the morphological development of living tissues, such as plants leaves. There are numerous choices that can be made in assembling a computer simulation of these growth system. We explore two main approaches, one where a reaction-diffusion simulation is first run with the results used to identify regions of growth, and the other where we simulate shell growth concurrently with a reaction-diffusion simulation in the manifold. We demonstrate that a variety of interesting shapes can be grown in this manner, and provide some intuition to the challenging problem of associating changes in parameter settings with the final shape.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.931

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 head0.023
GPT teacher head0.220
Teacher spread0.197 · 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