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Record W2888547573 · doi:10.1029/2018je005652

Lunar Cold Spots and Crater Production on the Moon

2018· article· en· W2888547573 on OpenAlexaff
J. P. Williams, J. L. Bandfield, D. A. Paige, Tyler Powell, B. T. Greenhagen, Scott M. Taylor, P. O. Hayne, E. J. Speyerer, R. R. Ghent, E. S. Costello

Bibliographic record

VenueJournal of Geophysical Research Planets · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of Toronto
FundersEli Lilly and Company
KeywordsImpact craterGeologyEjectaSpotsRegolithCold spotPopulationAstrobiologyAstronomyPhysicsChemistry

Abstract

fetched live from OpenAlex

Abstract Mapping of lunar nighttime surface temperatures has revealed anomalously low nighttime temperatures around recently formed impact craters on the Moon. The thermophysically distinct “cold spots” provide a way of identifying the most recently formed impact craters. Over 2,000 cold spot source craters were measured with diameters ranging from 43 m to 2.3 km. Comparison of the crater size‐frequency distribution with crater chronology models and crater counts of superposed craters on the ejecta of the largest cold spot craters constrains the retention time of the cold spots to no more than ~0.5–1.0 Myr with smaller cold spots possibly retained for only few hundred kyr. This would suggest a relatively rapid impact gardening rate with regolith overturn depths exceeding ~5 cm over this time scale. We observe a longitudinal heterogeneity in the cold spot distribution that reflects the Moon's synchronous rotation with a higher density of cold spots at the apex of motion. The magnitude of the asymmetry indicates the craters formed from a population of objects with low mean encounter velocities ~8.4 km/s. The larger cold spots ( D > 800 m) do not follow this trend, and are concentrated on the trailing farside. This could result from a shorter retention time for larger cold spots on the leading hemisphere due to the greater number of smaller, superposed impacts. Alternatively, the abundance of large cold spots on the trailing farside resulted from a swarm of 100‐m‐scale impactors striking the Moon within the last ~0.5 Myr.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.062
GPT teacher head0.326
Teacher spread0.264 · 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 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

Citations50
Published2018
Admission routes1
Has abstractyes

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