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Record W4408428887 · doi:10.5194/egusphere-egu25-14816

First results from the DORN experiment onboard the Chang’E 6 mission

2025· preprint· en· W4408428887 on OpenAlexaff
Pierre-Yves Meslin, Huaiyu He, Jiannan Li, Íñigo de Loyola Chacartegui Rojo, Bing Qi, Vincent Thomas, O. Gasnault, Zhizhong Kang, King Wah Wong, Luo Baorui, Sylvestre Maurice, P. Pilleri, Benoît Sabot, Jean‐Christophe Sabroux, Frédéric Girault, J. F. Pineau, J. Lasue, Patrick Pinet, Ding Zhang, Yang Ruihong

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsCanadian Aeronautics and Space Institute
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

The DORN instrument is an alpha spectrometer that was deployed to the surface of the Moon aboard the Chang’E 6 spacecraft in June 2024, in the Apollo crater within the South Polar Aitken Basin, at a latitude of -41.6°S. Its purpose was to measure the concentration of radon and polonium released from the lunar regolith, to study the origin and dynamics of the lunar exosphere and the physical and thermal properties of the regolith. It consisted of 16 silicon detectors, organised in 8 Detection Units and 2 Fields of view, covering the near and far fields, and measuring charged particles in the 0.6 to 12 MeV energy range. The instrument was switched on several times during the mission. First, during the Chang’E 6 Earth-Moon transfer (for 10 hours), then in an ellipitical orbit (for 32 hours) and in a circular orbit at an altitude of ~200 km (for 111 hours), in the wake of a strong solar storm. After Chang’E 6 landing, it collected 19 hours of data and was switched off a few hours before the liftoff of the ascent module. We will present the results obtained by this instrument and compare them with previous measurements of radon and polonium made from the orbit and with simulations obtained by a global model of radon transport in the lunar subsurface and exosphere.   

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.001
Research integrity0.0000.000
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 head0.045
GPT teacher head0.289
Teacher spread0.244 · 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.

Study designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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