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Record W2286684924 · doi:10.9753/icce.v33.posters.12

MODEL FOR PREDICTING BEACH CHANGES USING CELLULAR AUTOMATON METHOD

2012· article· en· W2286684924 on OpenAlex
Masatoshi Endo, Aki Kobayash, T. Uda, M. Serizawa, Yasuhito NOSHI

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

VenueCoastal Engineering Proceedings · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsCybernet Systems Corporation (Canada)
Fundersnot available
KeywordsSwashCellular automatonGeologyShoreDeposition (geology)Geotechnical engineeringSediment transportRevetmentPlageBeach morphodynamicsSurf zoneGeomorphologyMathematicsOceanographySediment

Abstract

fetched live from OpenAlex

Sand deposition on the gently-sloping revetment, the slope of which is steeper than the equilibrium slope of sand, is often observed when storm waves ran up the beach. Serizawa et al. (2006) have developed the BG model, in which the cross-shore sand transport depends on the balance between the equilibrium slope of sand and the local slope of the beach, and seaward sand transport will occur when the local slope of the beach or the structure is larger than the equilibrium slope. This implies that shoreward sand transport on the slope steeper than the equilibrium slope of sand cannot be predicted by the BG model. This is because the fundamental equation of the BG model is expressed by the net sand transport defined by the sum of the sand transport under the ongoing and outgoing waves. In this study, sand transport under the ongoing and outgoing waves is independently taken into account, and a new model for predicting beach changes is developed using the cellular automaton method.

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: none
Teacher disagreement score0.382
Threshold uncertainty score0.754

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 head0.028
GPT teacher head0.239
Teacher spread0.211 · 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