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Record W2331555101 · doi:10.2514/6.2012-358

A Mining Engineering Approach to Mining Lunar Regolith

2012· article· en· W2331555101 on OpenAlex
Paul Kluge, Arthur Neale

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsRegolithWork (physics)Mining industryMining engineeringComputer scienceEngineeringAstrobiologyMechanical engineering

Abstract

fetched live from OpenAlex

The strategic approach to mine planning and mining method and equipment selection used for the study of new mining operations on Earth is described and used in this paper. It is recommended by the authors that a similar diligent mining engineering approach should be followed when investigating potential mining projects on the Moon, particularly as the engineering work on these projects is expected to be side-tracked by logistical, processing and transport constraints. Mining engineering evaluations of an ore body should follow the stages discussed in this paper. The applicability of these stages to mining on the Moon was illustrated by providing an example investigation of mining lunar regolith with methods currently used for mineralized sand operations on Earth.

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.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.002
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 head0.248
Teacher spread0.224 · 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