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Record W2096307598 · doi:10.1088/1674-4527/12/9/010

New radio observations of the Moon at<i>L</i>band

2012· article· en· W2096307598 on OpenAlexaff
Xizhen Zhang, A. D. Gray, Yan Su, JunDuo Li, T. L. Landecker, Hongbo Zhang, Chunlai Li

Bibliographic record

VenueResearch in Astronomy and Astrophysics · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsDominion Astrophysical Observatory
Fundersnot available
KeywordsPhysicsBrightness temperatureAstrophysicsBrightnessEquatorRadio telescopeRegolithPolarization (electrochemistry)TelescopeAstronomyObservatoryDegree of polarizationLatitudeOptics

Abstract

fetched live from OpenAlex

We present results of new radio observations of the Moon at L band with the synthesis telescope of the Dominion Radio Astrophysical Observatory Synthesis Telescope. The resolution and temperature sensitivity of the observations are 159″ × 87″ and 1.7K, respectively. The main results are: (1) the lunar brightness temperature averaged over the whole disk is about 233 K while the average brightness temperature for the four quadrants are 228.1K (NE), 239.7K (NW), 233.9K (SW) and 228.8K (SE). The observations reveal large temperature and spatial variations on the Moon for the first time. The highest brightness temperature is about 257 K and it is located along the lunar equator, to the west. The total uncertainty is about 5% due to the absolute accuracy of the fluxes of the primary calibrators; (2) the total degree of polarization is about 6%. Both polarization intensity and degree of polarization increase from the disk's center to the limb, and the distribution of the degree of polarization along the limb is not uniform; (3) the new data are used to study the properties of regolith, such as dielectric constant and thickness distribution. The results show that the lunar regolith's thickness increases from the NW (mare area) to the SE (highland area) regions on 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)0.000
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 score0.134
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.060
GPT teacher head0.302
Teacher spread0.242 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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