MétaCan
Menu
Back to cohort
Record W2998643828 · doi:10.1002/mma.6205

Production of nitric oxide by a fragmenting bolide: An exploratory numerical study

2020· article· en· W2998643828 on OpenAlexaff
M. L. Niculescu, Elizabeth A. Silber, Reynold E. Silber

Bibliographic record

VenueMathematical Methods in the Applied Sciences · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsNova Scotia HospitalWestern University
Fundersnot available
KeywordsMeteoroidShock waveAtmosphere (unit)MechanicsPhysicsShock (circulatory)Computational fluid dynamicsPopulationEnvironmental scienceAstrobiologyAtmospheric sciencesMeteorology

Abstract

fetched live from OpenAlex

A meteoroid's hypersonic passage through the Earth's atmosphere results in ablational and fragmentational mass loss. Potential shock waves associated with a parent object as well as its fragments can modify the surrounding atmosphere and produce a range of physico‐chemical effects. Some of the thermally driven chemical and physical processes induced by meteoroid‐fragment‐generated shock waves, such as nitric oxide (NO) production, are less understood. Any estimates of meteoric NO production depend not only on a quantifiable meteoroid population and a rate of fragmentation, with a size capable of producing high temperature flows, but also on understanding the physical properties of the meteor flows along with their thermal history. We performed an exploratory pilot numerical study using ANSYS Fluent, the computational fluid dynamics (CFD) code, to investigate the production of NO in the upper atmosphere by small meteoroids (or fragments of meteoroids after they undergo a disruption episode) in the size range from 10 −2 to 1 m. Our model uses the simulation of a spherical body in the continuum flow at 70‐ and 80‐km altitude to approximate the behaviour of a small meteoroid capable of producing NO. The results presented in this exploratory study are in good agreement with previous studies.

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)0.004
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0010.000
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 head0.071
GPT teacher head0.361
Teacher spread0.290 · 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 designQualitative
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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueMathematical Methods in the Applied SciencesSame topicAstro and Planetary ScienceFrench-language works237,207