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Record W3159425117

Hydrostatic Balance in Meteorology

2020· article· en· W3159425117 on OpenAlexaffabout
Ana Mucalica

Bibliographic record

VenueStudent Research Proceedings · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsMacEwan University
Fundersnot available
KeywordsHydrostatic equilibriumAtmosphere (unit)MeteorologyHydrostatic pressureMathematicsEnvironmental scienceGeologyGeographyPhysicsMechanics
DOInot available

Abstract

fetched live from OpenAlex

Meteorology is a branch of geophysics that studies the properties of the atmosphere that “cushions” the Earth and all the phenomena that happen within it. The hydrostatic balance occurs when the pressure at any point in the fluid equals the weight of an air column of the unit section from above the point, and in these ideal conditions, we have the hydrostatic equation for the fluid in hydrostatic balance. The hydrostatic equation is viewed as the hydrostatic equilibrium condition, which provides an accurate approximation for the vertical dependence of the pressure field in the real atmosphere. In this talk, we manipulate a mathematical model describing the variation of pressure to altitude in the atmosphere using partial differential equations, and we will show how realistic charts based on real data are obtained, and compare them to charts obtained by using a climate change scenario when the temperature at sea-level is warmer by 2℃ than the one used in current charts. Work done in collaboration with Anneliese Ansorger, Cory Efird, Cassandra Lisitza, Ghristopher Macyk, Tarig Mergani, Adam Reeves, Rebecca Walton, Yaying Zhong, and Jett Ziehe, MacEwan University. Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled.) Exhibition held in the Gray Gallery at MacEwan University. Faculty Mentor: Ion Bica Department: Mathematics & Statistics

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)0.001
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 score0.060
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.162
GPT teacher head0.381
Teacher spread0.219 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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