MétaCan
Menu
Back to cohort
Record W2180657732 · doi:10.1175/jam2525.1

Formulation of an Index for Sea Breezes in Opposing Winds

2007· article· en· W2180657732 on OpenAlexafffund
Aurore Porson, D. G. Steyn, G. Schayes

Bibliographic record

VenueJournal of Applied Meteorology and Climatology · 2007
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaFédération Wallonie-BruxellesFonds pour la Formation à la Recherche dans l’Industrie et dans l’AgricultureFonds De La Recherche Scientifique - FNRS
KeywordsSea breezeMesoscale meteorologyScalingEnvironmental scienceClimatologyMeteorologyWind speedIndex (typography)GeologyGeographyMathematics

Abstract

fetched live from OpenAlex

Abstract A scaling analysis is conducted to explore the dependence of sea-breeze speed and inland occurrence in the presence of opposing winds on a set of dynamical parameters. The overall aim of the analysis is to develop an index for sea-breeze occurrence in the face of opposing winds, similar to the Biggs and Graves lake-breeze index. Most studies separate sea-breeze speed and sea-breeze inland occurrence or, at best, link the two in linear analyses. This work analyzes the output of a nonlinear numerical mesoscale model (in idealized simulations) using scaling methods commonly applied in observational studies. It is found that the scaled sea-breeze speed, in response to increasing magnitude of opposing wind, shows two distinct phases: a phase of increasing speed while the sea breeze progresses inland and a phase of sharply decreasing speed when the sea breeze is no longer detected inland. The analysis also allows the development of an index for sea-breeze inland occurrence. This index is an improvement over existing analyses through the use of nonlinear scaling and the use of surface heat flux as opposed to simpler land–sea temperature contrasts.

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)0.002
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 score0.069
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.021
GPT teacher head0.268
Teacher spread0.247 · 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

Citations42
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied Meteorology and ClimatologySame topicMeteorological Phenomena and SimulationsFrench-language works237,207