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Record W2051686719 · doi:10.5555/1400549.1400703

Implementing the SCIDDICA landslide model in Cell-DEVS

2008· article· en· W2051686719 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

VenueSpring Simulation Multiconference · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsCarleton University
Fundersnot available
KeywordsDEVSComputer scienceCellular automatonAutomatonFormalism (music)LandslideDebrisDistributed computingTheoretical computer scienceSimulationModeling and simulationAlgorithmEngineeringPhysics

Abstract

fetched live from OpenAlex

The SCIDDICA model is a custom cellular automata used to simulate and analyse landslides and debris flows. It models landslides by tracking the amounts of kinetic energy and debris in the slide and using a set of simple rules to determine the movement of the kinetic energy and debris from cell to cell.CD++ is a toolkit for implementing DEVS and Cell-DEVS models. We present an attempt to implement SCIDDICA using DEVS and CD++. A DEVS based definition of the SCID-DICA model introduces the advantages of the DEVS formalism to the model, and would allow formal verification of the DEVS definition of the model. This also permits us to integrate a SCIDDICA based landslide with other DEVS models, allowing larger and more complex simulations to be developed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.428

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 head0.026
GPT teacher head0.259
Teacher spread0.233 · 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