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Record W4282964754

Porous Flow and Sediment Transport Simulation for Physically-Based Weathering

2022· preprint· en· W4282964754 on OpenAlexaff
Théo Jonchier, Arthur Cavalier, Thibault Tricard, Guillaume Gilet, Stéphane Mérillou

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2022
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsWeatheringSediment transportSedimentFlow (mathematics)Geotechnical engineeringGeologyEnvironmental sciencePorosityHydrology (agriculture)Soil scienceGeomorphologyComputer scienceMechanicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

In the graphic community, rendering lifelike scenes remains an open challenge. Among the features required to reach photorealism, aging is the crucial detail that breaks the pristine aspect that is rarely observed in real life. Multiple approaches have been proposed, from example-based to physical simulation, to compute the aging of an object. In this context, we present a simulation framework that handles multiple weathering effects causing an object to alter over time. We identify the key phenomena (wetting, drying, erosion, deposition, and dissolution) and propose a base framework to address the aging process. To tackle this challenge, our method adapts a Smooth Particle Hydrodynamics model to represent external fluids (i.e., classical fluid simulation) and internal porous flow. Our framework handles the wetting, drying, and flow in the porous space in a unified approach. We extend pre-existing sediment transport method to allow the sediment transport outside and inside the porous space. With our method, sediments can be eroded from a solid surface, transported across solid objects through their porous space, and deposited to modify other objects’ properties across the scene.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.518
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.219
Teacher spread0.201 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2022
Admission routes1
Has abstractyes

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