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Record W2041790123 · doi:10.1029/2006gl025785

Role of plasma waves in Mars' atmospheric loss

2006· article· en· W2041790123 on OpenAlexaff
R. E. Ergun, L. Andersson, W. K. Peterson, D. A. Brain, G. T. Delory, D. L. Mitchell, R. P. Lin, A. W. Yau

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMars Exploration ProgramIonosphereAtmosphere of MarsSolar windPolar windPhysicsAtmospheric sciencesPlasmaGeophysicsAtmosphere (unit)OutflowPopulationExosphereAstrobiologyIonEnvironmental scienceMagnetopauseMeteorologyMartian

Abstract

fetched live from OpenAlex

Recent observations of plasma waves, electron fluxes, and ion fluxes in Mars' ionosphere indicate that ion heating may have had a significant impact on Mars' atmospheric loss. We discuss two energy sources of plasma waves: the solar wind interaction with Mars and field‐aligned currents in regions of crustal magnetic fields. These plasma waves can damp through cyclotron resonance with the O+ population in the ionosphere leading to heating and subsequent O+ escape supporting the ∼1025 atoms s−1 (∼0.4 kg/s) O+ outflow indicated by present‐day observations. A stronger solar wind and O+ source of ∼4 Gyr ago could support losses of ∼100 kg/s, enough to strip Mars' atmosphere or 10 m of water in a ∼0.3 Gyr period. The observational evidence for ion heating is, with current data sets, largely circumstantial so we suggest needed observations.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 studies0.0010.000
Scholarly communication0.0010.001
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 head0.014
GPT teacher head0.259
Teacher spread0.245 · 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

Citations89
Published2006
Admission routes1
Has abstractyes

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