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Record W2743550821 · doi:10.1175/jpo-d-16-0280.1

Buoy Measurements of Wind–Wave Relations during Hurricane Matthew in 2016

2017· article· en· W2743550821 on OpenAlexaff
S. A. Hsu, Yijun He, Hui Shen

Bibliographic record

VenueJournal of Physical Oceanography · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsBedford Institute of Oceanography
Fundersnot available
KeywordsDimensionless quantitySignificant wave heightBuoyWind waveMeteorologySea stateWave heightWind speedWind wave modelWavelengthStormGeologyWind profile power lawPhysicsAtmospheric sciencesEnvironmental scienceMechanicsOceanographyOptics

Abstract

fetched live from OpenAlex

Abstract Studies suggested that neutral-stability wind speed at 10 m U 10 ≥ 9 m s −1 and wave steepness H s / L p ≥ 0.020 can be taken as criteria for aerodynamically rough ocean surface and the onset of a wind sea, respectively; here, H s is the significant wave height, and L p is the peak wavelength. Based on these criteria, it is found that, for the growing wind seas when the wave steepness increases with time during Hurricane Matthew in 2016 before the arrival of its center, the dimensionless significant wave height and peak period is approximately linearly related, resulting in U 10 = 35 H s / T p ; here, T p is the dominant or peak wave period. This proposed wind–wave relation for aerodynamically rough flow over the wind seas is further verified under Hurricane Ivan and North Sea storm conditions. However, after the passage of Matthew’s center, when the wave steepness was nearly steady, a power-law relation between the dimensionless wave height and its period prevailed with its exponent equal to 1.86 and a very high correlation coefficient of 0.97.

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)0.000
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 score0.010
Threshold uncertainty score0.325

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 head0.025
GPT teacher head0.238
Teacher spread0.213 · 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

Citations17
Published2017
Admission routes1
Has abstractyes

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