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Record W2021694292 · doi:10.1002/2014gl060329

High manganese concentrations in rocks at Gale crater, Mars

2014· article· en· W2021694292 on OpenAlexaff
N. Lanza, Woodward W. Fischer, R. C. Wiens, J. P. Grotzinger, A. Ollila, A. Cousin, R. B. Anderson, B. C. Clark, R. Gellert, N. Mangold, S. Maurice, Stéphane Le Mouëlic, M. Nachon, M. E. Schmidt, Jeffrey A. Berger, S. M. Clegg, O. Forni, C. Hardgrove, Noureddine Melikechi, H. E. Newsom, V. Sautter

Bibliographic record

VenueGeophysical Research Letters · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsWestern UniversityBrock UniversityUniversity of Guelph
FundersCentre National d’Etudes SpatialesNational Aeronautics and Space Administration
KeywordsManganeseMars Exploration ProgramOxidizing agentGeologyRedoxMartianImpact craterMartian surfaceFERRIC IRONBasaltAstrobiologyGeochemistryMineralogyChemistryInorganic chemistryFerrous

Abstract

fetched live from OpenAlex

Abstract The surface of Mars has long been considered a relatively oxidizing environment, an idea supported by the abundance of ferric iron phases observed there. However, compared to iron, manganese is sensitive only to high redox potential oxidants, and when concentrated in rocks, it provides a more specific redox indicator of aqueous environments. Observations from the ChemCam instrument on the Curiosity rover indicate abundances of manganese in and on some rock targets that are 1–2 orders of magnitude higher than previously observed on Mars, suggesting the presence of an as‐yet unidentified manganese‐rich phase. These results show that the Martian surface has at some point in time hosted much more highly oxidizing conditions than has previously been recognized.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.025
GPT teacher head0.282
Teacher spread0.257 · 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 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

Citations113
Published2014
Admission routes1
Has abstractyes

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