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Record W4404635101 · doi:10.1073/pnas.2416680121

Metamorphism in a wet Martian middle crust

2024· letter· en· W4404635101 on OpenAlexaff
Richard M. Palin, Jon Wade, Brendan Dyck

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2024
Typeletter
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMartianMetamorphismCrustGeologyEarth scienceAstrobiologyGeochemistryPetrologyMars Exploration Program

Abstract

fetched live from OpenAlex

The article by Wright et al. ( 1 ) is the latest in a series of papers suggesting that Mars' inventory of liquid water was sequestered into the crust, rather than entirely lost to space ( 2 , 3 ).The authors constrain the presence of water in the mid crust (~11.5 to 20 km depth) by comparing bulk-rock geophysical data obtained from the InSight lander with a petrophysical model using a linear combination of putative igneous compositions (mafic basalt to felsic anorthosite).Using these parameters, the "best fit" to the InSight data is a water saturated (γ w = 100%) mid-crust.Although performing an inversion to determine physical properties from seismic data is an appropriate approach, the geological assumptions upon which it is based are less sound.Martian areotherms indicate a minimum increase in temperature with depth of 12 °C/km, but potentially as high as 20 °C/km ( 4 ).A median value of 16 °C/km equates to midcrustal temperatures of ~180 to 320 °C, placing the equilibrated conditions firmly within the metamorphic domain ( Fig. 1 ).Terrestrial field studies ( 5 ), laboratory analyses ( 6 ), and petrological modeling ( 2 , 4 ) show that hydrated igneous

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0030.001
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 head0.059
GPT teacher head0.272
Teacher spread0.213 · 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 designSimulation or modeling
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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

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