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Record W2998827475 · doi:10.1126/science.aay9522

Stormy water on Mars: The distribution and saturation of atmospheric water during the dusty season

2020· article· en· W2998827475 on OpenAlexfundno aff
Anna Fedorova, Franck Montmessin, Oleg Korablev, Mikhail Luginin, Аlexander Trokhimovskiy, Denis Belyaev, N. Ignatiev, Franck Lefèvre, Juan Alday, P. G. J. Irwin, Kevin Olsen, Jean‐Loup Bertaux, Ehouarn Millour, Anni Määttänen, Alexey Shakun, Аlexey Grigoriev, Andrey Patrakeev, Svyatoslav Korsa, Nikita Kokonkov, Lucio Baggio, F. Forget, Colin Wilson

Bibliographic record

VenueScience · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Education and Science of the Russian FederationScience and Technology Facilities CouncilAgence Nationale de la RechercheUK Space Agency
KeywordsMars Exploration ProgramEnvironmental scienceSaturation (graph theory)Atmospheric sciencesAstrobiologyGeologyPhysicsMathematics

Abstract

fetched live from OpenAlex

The loss of water from Mars to space is thought to result from the transport of water to the upper atmosphere, where it is dissociated to hydrogen and escapes the planet. Recent observations have suggested large, rapid seasonal intrusions of water into the upper atmosphere, boosting the hydrogen abundance. We use the Atmospheric Chemistry Suite on the ExoMars Trace Gas Orbiter to characterize the water distribution by altitude. Water profiles during the 2018-2019 southern spring and summer stormy seasons show that high-altitude water is preferentially supplied close to perihelion, and supersaturation occurs even when clouds are present. This implies that the potential for water to escape from Mars is higher than previously thought.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.190
Teacher spread0.182 · 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 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

Citations176
Published2020
Admission routes1
Has abstractyes

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