MétaCan
Menu
Back to cohort
Record W1444955853 · doi:10.1115/omae2009-79973

Estimation of Small-Scale Vertical Diffusivity for CO2 Injected in the Deep Ocean

2009· article· en· W1444955853 on OpenAlexfundno aff
Shinichiro Hirabayashi, Toru Sato

Bibliographic record

VenueVolume 4: Ocean Engineering; Ocean Renewable Energy; Ocean Space Utilization, Parts A and B · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
FundersCanadian Academy of Sport and Exercise Medicine
KeywordsWavenumberDissipationFilter (signal processing)Thermal diffusivityForcing (mathematics)Fast Fourier transformField (mathematics)Scale (ratio)GeologyComputational physicsMechanicsPhysicsMathematicsOpticsComputer scienceAlgorithmAtmospheric sciencesThermodynamics

Abstract

fetched live from OpenAlex

In this study, vertical diffusivity, the scale of which was O (10 m), at a particular site in the deep ocean was estimated by using numerical simulations with forcing low-wavenumber components, which had been reproduced from measurement data. Spatial information of velocity field was reproduced by spectral analysis of 4 sets of time-series measured simultaneously at different places in the real ocean. In order to estimate finer-scale structures, which are necessary to obtain statistical quantities such as energy dissipation rate, large eddy simulations were carried out with forcing low-wavenumber components of velocity reproduced in the spectral analysis. The low-wavenumber components generated by the nonlinear interaction of forced components and resolved components were successfully removed from the computational domain by introducing a partial spectral filter in place of the conventional FFT filter. Vertical diffusivity was estimated by using the energy dissipation rate of the reproduced flow field, which was 3.3×10−5 m2s−1 on the time average.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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.

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
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueVolume 4: Ocean Engineering; Ocean Renewable Energy; Ocean Space Utilization, Parts A and BSame topicOcean Acidification Effects and ResponsesFrench-language works237,207