MétaCan
Menu
Back to cohort
Record W1938867268 · doi:10.1029/2009gl041523

Simulating observed boundary layer clouds on Mars

2010· article· en· W1938867268 on OpenAlexaff
Frank Daerden, J. A. Whiteway, Richard Davy, C. Verhoeven, L. Komguem, C. Dickinson, Patrick Taylor, N. Larsen

Bibliographic record

VenueGeophysical Research Letters · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
Fundersnot available
KeywordsMars Exploration ProgramWater vaporDiurnal cycleAtmospheric sciencesPlanetary boundary layerPrecipitationEnvironmental scienceDaytimeLidarBoundary layerWater cycleGeologyMeteorologyAstrobiologyPhysicsRemote sensingMechanics

Abstract

fetched live from OpenAlex

A microphysical model for Mars dust and ice clouds has been applied in combination with a model of the planetary boundary layer (PBL) for the interpretation of measurements by the LIDAR instrument on the Phoenix Mars mission. The model simulates nighttime clouds and fall streaks within the PBL that are similar in structure to the LIDAR observations. The observed regular daily pattern of water ice cloud formation and precipitation at the top of the PBL is interpreted as a diurnal process in the local water cycle in which precipitation of large ice crystals (30–50 μm effective radius) results in downward transport of water vapor within the PBL. This is followed by strong vertical mixing during daytime, and this cycle is repeated every sol to confine water vapor within the PBL.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.078
GPT teacher head0.329
Teacher spread0.252 · 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

Citations42
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueGeophysical Research LettersSame topicPlanetary Science and ExplorationFrench-language works237,207