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Record W4200430377 · doi:10.1002/essoar.10509618.1

The effect of surface dust availability on the timing of Martian dust storms.

2021· preprint· en· W4200430377 on OpenAlexaff
Christopher Lee, M. I. Richardson, Claire Newman

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of Toronto
FundersNational Aeronautics and Space Administration
KeywordsMartianStormAstrobiologyDust stormMars Exploration ProgramEnvironmental scienceMeteorologyPhysics

Abstract

fetched live from OpenAlex

Current state-of-the-art models of dust lifting in Mars climate models track finite surface-dust reservoirs but use a constant lifting coefficient at all locations until the reservoir is exhausted (e.g. Newman and Richardson, 2015). In this work, the MarsWRF General Circulation Model (GCM) is modified to adjust the dust lifting coefficient as a function of a dust availability parameter that varies with location. A “Dust Cover Index” derived from remote albedo observations is used to limit the availability of dust on the surface, without limiting the total mass of dust in the reservoir. Simulations with a two moment scheme (Lee et al., 2018) are compared with a nominal case where the unlimited dust is equally available across the planet. Idealized simulations are then used to show that the spatial location of dust availability controls the timing of large dust storms during the annual storm season, and this relationship may be inverted to provide a weak constraint on the lifting locations that lead to observed dust storms on Mars.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.238
Teacher spread0.216 · 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 designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

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