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Record W3216820592

Examining Pressure Instability in the Troposphere in a Climate Change Scenario

2021· article· en· W3216820592 on OpenAlexaff
Cassandra Lisitza

Bibliographic record

VenueStudent Research Proceedings · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsMacEwan University
Fundersnot available
KeywordsAtmosphere (unit)TroposphereAtmospheric sciencesEnvironmental scienceAtmospheric pressureGreenhouse gasAdiabatic processLow-pressure areaAtmospheric modelClimate changeInstabilityAtmospheric temperatureAltitude (triangle)ClimatologyAtmosphere of EarthLapse rateMeteorologyMechanicsGeologyGeographyThermodynamicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

Meteorology is a branch of geophysics concerned with the atmosphere's processes and phenomena and atmospheric effects on our weather. The atmospheric pressure depends on the temperature, and hence, temperature variations cause pressure instability, which directly impacts humans. Earth's climate is severely affected by anthropogenic emissions, which cause an increased concentration of greenhouse gases in the atmosphere. We analyzed the effect greenhouse gases have on the atmosphere's pressure by considering the temperature of dry air, the virtual temperature of moist air, and the virtual temperature for CO2-rich air. In this talk, we manipulate a mathematical model describing the variation of atmospheric pressure to altitude for the three temperature scenarios, using partial differential equations. We will also demonstrate how atmospheric pressure instability correlates to climate change. Further, we utilize the model describing the variation of pressure to altitude in the adiabatic system's three temperature scenarios. Department: Mathematical Sciences  Faculty Mentor: Dr. Ion Bica

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.087
GPT teacher head0.347
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

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