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Record W4408385257 · doi:10.1029/2024ea003704

Connections Between Meteor Persistent Trains and Ozone Content in the Mesopause Region

2025· article· en· W4408385257 on OpenAlexaff
L. E. Cordonnier, K. S. Obenberger, J. M. Holmes, G. B. Taylor, Denis Vida

Bibliographic record

VenueEarth and Space Science · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
FundersAir Force Office of Scientific ResearchNational Science Foundation
KeywordsMesopauseMeteor (satellite)OzoneEnvironmental scienceTrainMeteorologyAtmospheric sciencesMesosphereGeologyGeographyStratosphereCartography

Abstract

fetched live from OpenAlex

Abstract Ozone () is an important trace species in the mesopause region of Earth's atmosphere and is difficult to directly probe. We found that the percentage of sporadic meteors that produced persistent trains (PTs) exhibit semiannual variations which are strongly correlated with those of the average peak volume mixing ratio (vmr) of the secondary ozone maximum (near the mesopause, 90–95 km). PTs are long‐lasting, self‐emitting phenomena that occasionally form after a meteor, thought to arise from exothermic reactions between meteoric metals and atmospheric . The observed correlation between PT rates and essentially confirms ozone's necessity for the endurance of PTs in the continuum emission regime. Owing to this correlation, we were also able to develop a simple relationship between these two quantities providing an easy method of estimating in the mesopause region using the monthly sporadic PT occurrence rates. This represents a new, ground‐based technique for estimating content in the upper atmosphere. Meteor showers were much less correlated with due to their respective homogeneity, stressing the importance of intrinsic meteoroid properties for PT formation. Lastly, we examined the connection between content and the duration of PTs and found no clear correlation.

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 score0.038
Threshold uncertainty score0.269

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)0.0000.000

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 head0.030
GPT teacher head0.236
Teacher spread0.206 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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