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Record W2782881837 · doi:10.1002/2017je005314

Interannual and Diurnal Variability in Water Ice Clouds Observed from MSL Over Two Martian Years

2018· article· en· W2782881837 on OpenAlexafffund
Jacob L. Kloos, John E. Moores, J. A. Whiteway, Monika Aggarwal

Bibliographic record

VenueJournal of Geophysical Research Planets · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
FundersCanadian Space AgencyCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsNoonMorningMars Exploration ProgramOpacityMartianEnvironmental scienceAtmosphere of MarsAtmospheric sciencesPhotometry (optics)Diurnal temperature variationVariation (astronomy)ClimatologyMeteorologyAstrobiologyGeologyGeographyPhysicsAstronomyStars

Abstract

fetched live from OpenAlex

Abstract We update the results of cloud imaging sequences from the Mars Science Laboratory (MSL) rover Curiosity to complete two Mars years of observations ( L S =160° of Mars year (MY) 31 to L S =160° of MY 33). Relatively good seasonal coverage is achieved within the study period, with just over 500 observations obtained, averaging one observation every 2–3 sols. Cloud opacity measurements are made using differential photometry and a simplified radiative transfer method. These opacity measurements are used to assess the interannual variability of the aphelion cloud belt (ACB) for MY 32 and 33. Upon accounting for a statistical bias in the data set, the variation is found to be <30% within uncertainty. Diurnal variation of the ACB is also able to be examined in MY 33 owing to an increased number of early morning observations in this year. Although a gap in data around local noon prevents a complete assessment, we find that cloud opacity is moderately increased in the morning hours (07:00–09:00) compared to the late afternoon (15:00–17:00).

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)0.002
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 score0.156
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.335
Teacher spread0.289 · 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

Citations26
Published2018
Admission routes2
Has abstractyes

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