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Record W3046077464

Global Distribution of Lunar Impact Melt Flows

2014· article· en· W3046077464 on OpenAlexaff
C. D. Neish, Jack Madden, Lynn M. Carter, B. R. Hawke, T. A. Giguere, V. J. Bray, G. R. Osinski, J. T. S. Cahill

Bibliographic record

VenueLunar and Planetary Science Conference · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsWestern University
Fundersnot available
KeywordsImpact craterGeologyLunar cratersGeophysicsGeomorphologyAstrobiologyPhysics
DOInot available

Abstract

fetched live from OpenAlex

In this study, we analyzed the distribution and properties of 146 craters with impact melt deposits exterior to their rims. Many of these craters were only recently discovered due to their unusual radar properties in the near-global Mini-RF data set. We find that most craters with exterior deposits of impact melt are small, less than 20 kilometers, and that the smallest craters have the longest melt flows relative to their size. In addition, exterior deposits of impact melt are more common in the highlands than the mare. This may be the result of differing target properties in the highlands and mare, the difference in titanium content, or the greater variation of topography in the highlands. We find that 80 percent of complex craters and 60 percent of simple craters have melt directions that are coincident or nearly coincident with the lowest point in their rim, implying that pre-existing topography plays a dominant role in melt emplacement. This is likely due to movement during crater modification (complex craters) or breached crater rims (simple craters). We also find that impact melt flows have very high circular polarization ratios compared to other features on the Moon. This suggests that their surfaces are some of the roughest material on the Moon at the centimeter to decimeter scale, even though they appear smooth at the meter scale.

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 score0.197
Threshold uncertainty score0.444

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 communication0.0000.001
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 head0.011
GPT teacher head0.236
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 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

Citations0
Published2014
Admission routes1
Has abstractyes

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