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Record W3014849539

On thermally forced circulations over heated terrain

2013· article· en· W3014849539 on OpenAlexaff
Daniel J. Kirshbaum

Bibliographic record

VenueAGU Spring Meeting Abstracts · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsScalingBoundary layerMechanicsTerrainFlow (mathematics)Stability (learning theory)Planetary boundary layerMeteorologyPhysicsMathematicsGeometryComputer science
DOInot available

Abstract

fetched live from OpenAlex

AbstractA combination of analytical and numerical models is used to gain insight into the dynamics of thermally forced circulations over diurnally heated terrain. Solutions are obtained for two-layer flows (representing the boundary layer and the overlying free troposphere) over an isolated mountainlike heat source. A scaling based on the linearized Boussinesq system of equations is developed to quantify the strength of thermally forced updrafts and to identify three flow regimes, each with distinct dynamics and parameter sensitivities. This scaling closely matches corresponding numerical simulations in two of these regimes: the first characterized by a weakly stable boundary layer and significant background winds and the second by a strongly stable boundary layer. In the third regime, characterized by weak winds and weak boundary layer stability, this scaling is outperformed by a fundamentally different scaling based on thermodynamic heat engines. Within this regime, the inability of wind ventilation or ...

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 score0.009
Threshold uncertainty score0.018

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 studies0.0000.001
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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
Published2013
Admission routes1
Has abstractyes

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