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Record W2745099363 · doi:10.1109/rast.2017.8002977

Production of lunar soil simulant in Turkey

2017· article· en· W2745099363 on OpenAlexaboutno aff
Yusuf Cengiz Toklu, Ali Erdem Çerçevik, Süheyla Yerel Kandemir, Mustafa Özgür Yaylı

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
FundersNational Aeronautics and Space Administration
KeywordsLunar soilSoil waterGeology of the MoonEnvironmental scienceLunar mareAstrobiologyGeologyEarth scienceChinaGeochemistrySoil scienceMineralogyBasaltArchaeologyPhysicsGeography

Abstract

fetched live from OpenAlex

It is obvious that lunar structures will be constructed on lunar surface, and that the materials to be used are only lunar soil. That is why lunar soils has to be investigated thoroughly as to its physical, chemical and mechanical properties. Unfortunately there is not enough lunar rock or soil brought to Earth for being able to run these analyses. For this purpose, several simulants have been produced in countries like US, Canada, Japan, and China etc. This paper is about an attempt carried out for production of lunar soil simulant in Turkey. For this purpose, undegenerated soil samples are collected from relatively young regions of Turkey, like Kula Sandal Divlit region; the properties of these soils including their chemical properties are compared to properties of lunar soils. As a result of these comparisons, a mix is produced to represent lunar soil. It is hoped that this study will be repeated with more samples from other parts of Turkey to find other simulants to represent lunar soils and rocks in research about lunar constructions.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.246
Teacher spread0.226 · 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 designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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