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Record W2094586075 · doi:10.5194/acp-11-7955-2011

Lake breezes in the southern Great Lakes region and their influence during BAQS-Met 2007

2011· article· en· W2094586075 on OpenAlexaffabout
David Sills, Jeff Brook, Ilan Levy, Paul A. Makar, Jiangyi Zhang, Peter A. Taylor

Bibliographic record

VenueAtmospheric chemistry and physics · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsYork UniversityEnvironment and Climate Change Canada
FundersEnvironment and Health Fund
KeywordsSea breezeAir quality indexEnvironmental scienceClimatologyMesoscale meteorologyGeologyAtmospheric sciencesMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract. Meteorological observations from the BAQS-Met field experiment during the summer months of 2007 were integrated and manually analyzed in order to identify and characterize lake breezes in the southern Great Lakes region of North America, and assess their potential impact on air quality. Lake breezes occurred frequently, with one or more lake breezes identified on 90 % of study days. They affected all parts of the study region, including southwestern Ontario and nearby portions of southeast Lower Michigan and northern Ohio, with lake-breeze fronts occasionally penetrating from 100 km to over 200 km inland. Occurrence rates and penetration distances were found to be higher than previously reported in the literature. This comprehensive depiction of observed lake breezes allows an improved understanding of their influence on the transport, dispersion, and production of pollutants in this region. The observational analyses were compared with output from subsequent runs of a high-resolution numerical weather prediction model. The model accurately predicted lake breeze occurrence and type in a variety of synoptic wind regimes, but selected cases showed substantial differences in the detailed timing and location of lake-breeze fronts, and with the initiation of deep moist convection. Knowledge of such strengths and weaknesses aids in the interpretation of results from air quality models driven by this meteorological model.

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 score0.057
Threshold uncertainty score0.882

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)0.0010.000

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 head0.017
GPT teacher head0.183
Teacher spread0.166 · 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

Citations125
Published2011
Admission routes2
Has abstractyes

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