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Record W2990129389 · doi:10.1038/s41598-019-53142-0

Cooling history and emplacement of a pyroxenitic lava as proxy for understanding Martian lava flows

2019· article· en· W2990129389 on OpenAlexaboutno aff
Mara Murri, M. C. Domeneghetti, A. M. Fioretti, Fabrizio Nestola, Francesco Vetere, Diego Perugini, Alessandro Pisello, Manuele Faccenda, Matteo Alvaro

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
FundersMinistero dell’Istruzione, dell’Università e della RicercaEuropean CommissionAlexander von Humboldt-Stiftung
KeywordsLavaMars Exploration ProgramGeologyMartianGreenstone beltArcheanBasaltMantle (geology)VolcanoEarth scienceGeochemistryGeophysicsAstrobiologyPetrologyPhysics

Abstract

fetched live from OpenAlex

Terrestrial analogues are often investigated to get insights into the geological processes occurring on other planetary bodies. Due to its thickness and petrological similarities, the pyroxenitic layer of the 120m-thick magmatic pile Theo's Flow (Archean Abitibi greenstone belt Ontario, Canada), has always been regarded as the terrestrial analogue for Martian nakhlites. However, its origin and cooling history and, as a consequence those of nakhlites, have always been a matter of vigorous debate. Did this lava flow originate from a single magmatic event similar to those supposed to occur on Mars or do the different units derive from multiple eruptions? We demonstrate, by a combination of geothermometric constraints on augite single crystals and numerical simulations, that Theo's Flow has been formed by multiple magma emplacements that occurred at different times. This discovery supports the idea that the enormous lava flows with similar compositions observed on Mars could be the result of a process where low viscosity lavas are emplaced during multiple eruptions. This has profound implications for understanding the multiscale mechanisms of lava flow emplacement on Earth and other planetary bodies.

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 score0.495
Threshold uncertainty score0.286

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 integrity0.0000.000
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 head0.030
GPT teacher head0.236
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

Citations12
Published2019
Admission routes1
Has abstractyes

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