MétaCan
Menu
Back to cohort
Record W4386969153 · doi:10.9753/icce.v37.management.7

NUMERICAL SIMULATION OF DRIFTWOOD TRANSPORT BY WAVES IN A LABORATORY BASIN

2023· article· en· W4386969153 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

VenueCoastal Engineering Proceedings · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsUniversity of OttawaNational Research Council Canada
Fundersnot available
KeywordsStructural basinEnvironmental scienceHydrology (agriculture)Numerical modelsGeologyOceanographyGeomorphologyNumerical modelingGeotechnical engineeringGeophysics

Abstract

fetched live from OpenAlex

Driftwood plays an important role in coastal ecosystems but, in large accumulations, can have negative impacts and pose hazards to navigation, infrastructure and communities (Murphy et al. 2021). The ability to accurately predict the fate and transport of coastal driftwood is central to informing sustainable management practices. However, there is a paucity of numerical modelling studies focused on driftwood transport in coastal waters (Murphy et al. 2021). Models developed for rivers (e.g. Ruiz-Villanueva et al. 2014) lack consideration of processes affecting driftwood transport and accumulation in coastal environments, such as wind waves. Murphy et al. (2020) conducted experiments in a 50-m by 30-m wave basin, wherein model driftwood was released and tracked within a 1/30 scale physical model of a sandy beach system with various coastal structures. The tests revealed that several factors influence the mobility of coastal driftwood exposed to waves, beaching and washoff processes in particular.

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 score0.031
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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