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Record W4281727444 · doi:10.1029/2022gl098821

Planet‐Wide Ozone Destruction in the Middle Atmosphere on Mars During Global Dust Storm

2022· article· en· W4281727444 on OpenAlexaff
Frank Daerden, Lori Neary, M. J. Wolff, R. T. Clancy, Franck Lefèvre, J. A. Whiteway, S. Viscardy, Arianna Piccialli, Yannick Willame, C. Depiesse, Shohei Aoki, Ian Thomas, Bojan Ristic, Justin Erwin, Jean‐Claude Gérard, Brad J. Sandor, Alain Khayat, M. D. Smith, Jon Mason, Manish Patel, Gerónimo Villanueva, Giuliano Liuzzi, G. Bellucci, J. J. López‐Moreno, Ann Carine Vandaele

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
FundersBelgian Federal Science Policy Office
KeywordsDust stormAtmosphere (unit)Mars Exploration ProgramAtmosphere of MarsStormAtmospheric sciencesOccultationAstrobiologyWater vaporOrbiterEnvironmental scienceMartianTrace gasMiddle latitudesOzoneRadio occultationGeologyMeteorologyIonospherePhysicsGeophysicsAstronomy

Abstract

fetched live from OpenAlex

Abstract The Nadir and Occultation for MArs Discovery (NOMAD)/UV‐visible (UVIS) spectrometer on the ExoMars Trace Gas Orbiter provided observations of ozone (O 3 ) and water vapor in the global dust storm of 2018. Here we show in detail, using advanced data filtering and chemical modeling, how Martian O 3 in the middle atmosphere was destroyed during the dust storm. In data taken exactly 1 year later when no dust storm occurred, the normal situation had been reestablished. The model simulates how water vapor is transported to high altitudes and latitudes in the storm, where it photolyzes to form odd hydrogen species that catalyze O 3 . O 3 destruction is simulated at all latitudes and up to 100 km, except near the surface where it increases. The simulations also predict a strong increase in the photochemical production of atomic hydrogen in the middle atmosphere, consistent with the enhanced hydrogen escape observed in the upper atmosphere during global dust storms.

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 score0.273
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.274
Teacher spread0.232 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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