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Record W4308452876 · doi:10.1038/s43247-022-00602-7

Chemical weathering over hundreds of millions of years of greenhouse conditions on Mars

2022· article· en· W4308452876 on OpenAlexfundno aff
Binlong Ye, J. R. Michalski

Bibliographic record

VenueCommunications Earth & Environment · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
FundersResearch Grants Council, University Grants CommitteeCanadian Institute for Advanced Research
KeywordsWeatheringNoachianHesperianMars Exploration ProgramGeologyImpact craterMartianEarth scienceBedrockLeaching (pedology)Geologic recordGeochemistryAstrobiologyGeomorphologySoil scienceSoil water

Abstract

fetched live from OpenAlex

Abstract Chemical weathering profiles on Mars which consist of an upper Al clay-rich, Fe-poor layer and lower Fe/Mg clay-rich layer are believed to have formed due to precipitation-driven top down leaching process in an ancient, reducing greenhouse climate. Here we use remote sensing imagery and spectroscopy coupled with topographic data and crater chronology to explore the geological characteristics, stratigraphy and relative age of >200 weathering profiles across the southern highlands of Mars. We find that nearly all exposures show a similar, single stratigraphic relationship of Al/Si materials over Fe/Mg clays rather than multiple, interbedded mineralogical transitions. This suggests either one single climate warming event or, perhaps more likely, chemical resetting of weathering horizons during multiple events. While the time required to form a typical martian weathering profile may have been only ∼10 6 −10 7 years, the profiles occur in deposits dating from the Early Noachian into the Hesperian and suggest that chemical weathering may have occurred over a large range of geologic time, with a peak around 3.7–3.8 billion years ago.

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)0.000
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 score0.653
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.020
GPT teacher head0.226
Teacher spread0.206 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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