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Record W2407103080 · doi:10.1038/srep17075

Petrologic Characteristics of the Lunar Surface

2015· article· en· W2407103080 on OpenAlexaff
Xianmin Wang, Witold Pedrycz

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of Alberta
FundersWuhan Science and Technology ProjectFundamental Research Funds for the Central UniversitiesChina University of GeosciencesChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsGeologyBasaltVolcanismGeology of the MoonMantle (geology)CrustLunar mareGeochemistryIntrusionTectonicsPaleontology

Abstract

fetched live from OpenAlex

Petrologic analysis of the lunar surface is critical for determining lunar formation and evolution. Here, we report the first global petrologic map that includes the five most important lunar lithological units: the Ferroan Anorthositic (FAN) Unit, the Magnesian Suite (MS) Unit, the Alkali Suite (AS) Unit, the KREEP Basalt (KB) Unit and the Mare Basalt (MB) Unit. Based on the petrologic map and focusing on four long-debated and important issues related to lunar formation and evolution, we draw the following conclusions from the new insights into the global distribution of the five petrologic units: (1) there may be no petrogenetic relationship between MS rocks and KB; (2) there may be no petrogenetic link between MS and AS rocks; (3) the exposure of the KREEP component on the lunar surface is likely not a result of MB volcanism but is instead mainly associated with the combined action of plutonic intrusion, KREEP volcanism and celestial collision; (4) the impact size of the South Pole-Aitken basin is constrained, i.e., the basin has been excavated through the whole crust to exhume a vast majority of lower-crustal material and a very limited mantle components to the lunar surface.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.122

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 head0.025
GPT teacher head0.224
Teacher spread0.199 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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