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Record W4413439863 · doi:10.1016/j.icarus.2025.116788

Deposition of CO2 ice under Martian polar conditions: Textures, NIR reflectance and response to thermal stresses

2025· article· en· W4413439863 on OpenAlexafffund
R. Karimova, I. B. Smith

Bibliographic record

VenueIcarus · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsDeposition (geology)AstrobiologyPolarThermalReflectivityMaterials scienceAtmospheric sciencesGeologyRemote sensingEnvironmental scienceMeteorologyOpticsGeomorphologyAstronomy

Abstract

fetched live from OpenAlex

We created an experimental setup to simulate polar martian conditions and study the behavior of CO 2 ice. This paper summarizes the observations from the first year of operation of the MARs Volatile and Ice evolutioN Chamber (MARVIN) at York University (2020−2021). We observed three different textures of CO 2 ice deposited on the cold plate and recorded the reflectance of the ice in the near-infrared range of 1–2.5 μm throughout its deposition. Polycrystalline translucent slab ice formed in most experiments when the environmental conditions were closest to those on Martian poles, as reported by other authors (Portyankina et al., 2019; Schmitt et al., 2020). Additionally, we observed fine-grained CO₂ ice sintering and integrating into an existing translucent slab ice. We further discuss some results and challenges of measuring the reflectance of the translucent CO 2 ice and influence of the substrate on results. Finally, we recorded fracturing of CO 2 ice slab in an experimental run that simulated basal sublimation induced jet-like gas eruptions.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.009
GPT teacher head0.264
Teacher spread0.256 · 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 designBench or experimental
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

Citations2
Published2025
Admission routes2
Has abstractno

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