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Record W2106535042 · doi:10.1029/2006jc004013

Near‐bed turbulence and relict waveformed sand ripples: Observations from the inner shelf

2008· article· en· W2106535042 on OpenAlexaff
Alex E. Hay

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGeologyTurbulenceContinental shelfOceanographyMeteorologyGeography

Abstract

fetched live from OpenAlex

Results are presented from a field investigation of near‐bed turbulence above degrading waveformed sand ripples in 17‐m water depth on the inner shelf. The heights of the 50‐cm wavelength primary ripples were about 5 cm at the start of the observation period, and decreased by a factor of 2 within 15 days. The principal degradation mechanism involved fish making pits in the seafloor. Near‐bed turbulent kinetic energy dissipation rates are estimated both from the energy spectrum and from the vertical structure function within the inertial subrange, and ranged from 0.1 × 10 −6 to 3 × 10 −6 W/kg. The friction velocity, u * , at the bed ranged from 0.3 to 0.5 cm/s, and the wave friction factor, f w , from 0.017 to 0.02. The nearbed turbulence intensities and consequently the estimated values of u 2 * and f w are likely too small by a factor of 2, partly to satisfy the smooth‐wall constraint, and partly to account for the effects of small‐scale turbulence within the finite‐volume range cells of the coherent Doppler system used to make the turbulence estimates. Finally, the results indicate that the hydraulic roughness of relict ripples is likely a function of both ripple height and steepness, and that the relative roughness should also depend on the near‐bed wave orbital excursion. For modeling purposes, Nielsen's ripple roughness formula is recommended, with a reduced proportionality constant to account for the effects of irregular wave forcing and non‐equilibrium ripple history.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.045
GPT teacher head0.269
Teacher spread0.224 · 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 designObservational
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

Citations32
Published2008
Admission routes1
Has abstractyes

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