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Record W4225015808 · doi:10.1029/2022je007193

Carbon Dioxide Ice Glaciers at the South Pole of Mars

2022· article· en· W4225015808 on OpenAlexafffund
I. B. Smith, Nicole‐Jeanne Schlegel, Eric Larour, I. Isola, P. B. Buhler, N. E. Putzig, Ralf Greve

Bibliographic record

VenueJournal of Geophysical Research Planets · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
FundersCanada Research ChairsNational Aeronautics and Space Administration
KeywordsGeologyIce streamMars Exploration ProgramAntarctic sea iceIce divideSea ice thicknessSea iceMartianCryospherePolarGeomorphologyCarbon dioxidePancake iceAtmospheric sciencesClimatologyAstrobiologyPhysicsChemistry

Abstract

fetched live from OpenAlex

Abstract Massive, kilometer thick deposits of carbon dioxide (CO 2 ) ice have been detected at the south polar cap of Mars by radar investigations. These deposits are divided into several units that are separated by thin water ice bounding layers. Recent studies investigated the accumulation history of CO 2 ice and found that the deposits most likely formed during several episodes in the past, when Martian obliquity was much lower than now. Those studies, while able to predict total volumes of CO 2 ice consistent with those observed, did not attempt to explain the anomalous three‐dimensional distribution (thickness or extent) of CO 2 ice or the ice's offset from the topographic high of the polar cap. In this paper we use a combination of feature analysis and numerical modeling to demonstrate that the CO 2 deposits flow as glaciers and that glacial flow distributes the ice into its current position. Further, this distribution allows the ice to survive during high obliquity excursions.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.036
GPT teacher head0.297
Teacher spread0.261 · 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

Citations20
Published2022
Admission routes2
Has abstractyes

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