MétaCan
Menu
Back to cohort
Record W4404942759 · doi:10.1002/qj.4891

Study of fog dissipation in an internal boundary layer on Sable Island

2024· article· en· W4404942759 on OpenAlexaff
Stef L. Bardoel, Sebastian W. Hoch, Jesus Ruiz‐Plancarte, Luc Lenain, Ismail Gültepe, Andrey A. Grachev, S. Gaberšek, Qing Wang, Harindra J. S. Fernando

Bibliographic record

VenueQuarterly Journal of the Royal Meteorological Society · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsOntario Tech University
FundersOffice of Naval Research
KeywordsDissipationBoundary layerGeologyLayer (electronics)Environmental scienceMechanicsAtmospheric sciencesMaterials sciencePhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract An interesting fog dissipation event was observed during the Fog and Turbulence Interactions in the Marine Atmosphere (FATIMA) Grand Banks field campaign, where a fog‐free region appeared immediately downstream of Sable Island as fog advected past it. This fog‐free region was predicted a priori by a high‐resolution numerical model that guided intensive operational periods of the FATIMA campaign, and its presence was adumbrated by GOES satellite observations. A comprehensive set of field observations shows that this fog‐free layer was due to the development of a (daytime) thermal internal boundary layer (IBL) that grew with distance from the leading shore line. The net incoming radiation following sunrise led to an increased air temperature and decreased relative humidity close to the ground, thus dissipating fog over the island. The height of the thermal IBL, as identified by the thickness of the superadiabatic layer, was found to be consistent with several available theoretical IBL formulae.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.276
Teacher spread0.244 · 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 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

Citations8
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueQuarterly Journal of the Royal Meteorological SocietySame topicMeteorological Phenomena and SimulationsFrench-language works237,207