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Record W4401826356 · doi:10.5376/ijms.2024.14.0028

Impact of Ocean Waves on Atmospheric Boundary Layer Dynamics: Mechanisms and Observations

2024· article· en· W4401826356 on OpenAlexvenueno aff
Liping Liu

Bibliographic record

VenueInternational Journal of Marine Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAquatic life and conservation
Canadian institutionsnot available
Fundersnot available
KeywordsAquacultureSea cucumberFisheryTropical marine climateEnvironmental scienceGeographyFish <Actinopterygii>EcologyBiologyMeteorology

Abstract

fetched live from OpenAlex

Ocean waves can significantly impact the Atmospheric Boundary Layer (ABL) by altering wind speed distribution, momentum transfer, and energy exchange processes near the ocean surface. The stress induced by waves modifies the structure of the ABL, affects the stability of wind profiles, and through the propagation of momentum and turbulence, further influences the overall dynamics of the atmospheric boundary layer. This study systematically analyzes how ocean waves impact ABL dynamics through various mechanisms, validates these theoretical models against actual observational data, and, by integrating the latest observational technologies such as sLiDAR and satellite remote sensing, further explores the regional and seasonal variations in wave-ABL interactions. The study also evaluates how these changes affect climate and weather forecasting. In the context of global climate change, accurately simulating and predicting wind-wave interactions is crucial for climate adaptation and mitigation strategies. This research aims to improve the accuracy of models that simulate extreme weather events and provide scientific evidence for enhancing climate models, optimizing offshore renewable energy utilization, and managing marine resources.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.173

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 head0.024
GPT teacher head0.275
Teacher spread0.251 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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