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Record W2954825951 · doi:10.1029/2019je006003

The Temporal and Geographic Extent of Seasonal Cold Trapping on the Moon

2019· article· en· W2954825951 on OpenAlexafffund
Jacob L. Kloos, John E. Moores, J. Sangha, Tue Giang Nguyen, Norbert Schörghofer

Bibliographic record

VenueJournal of Geophysical Research Planets · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
FundersCanadian Space Agency
KeywordsSolsticeNorthern HemisphereLatitudePolarSouthern HemisphereAtmospheric sciencesSeasonalityClimatologyPolar vortexGeologyEnvironmental sciencePhysical geographyGeographyStratosphereGeodesyPhysicsEcology

Abstract

fetched live from OpenAlex

Abstract We assess the geographic distribution and temporal variability of seasonal shadow at the lunar polar regions and explore its influence on surface water migration and deposition within known permanently shadowed regions (PSRs) in the modern era. At its largest expanse near the winter solstice, seasonally shadowed area more than doubles the permanently shadowed area at both poles. The growth and decay of polar shadow throughout the year enforce distinct seasonal patterns in the poleward migration of water as well as a cyclical variation in the polar surface hydration throughout the year if a continual source of water is assumed. The polar surface water abundance peaks near the hemispheric vernal equinox—significantly offset from the solstice where the seasonal trapping area is most expansive—due to the retention of seasonally trapped water. Owing to their low areal density, lower‐latitude PSRs do not significantly hamper the poleward migration of water, enabling water to reach the high polar latitudes where cold trapping area is densest. We find that northern hemisphere PSRs accumulate more water per unit area than southern hemisphere PSRs and that this disparity is especially prominent beyond 85°. The north/south asymmetry is attributed to differences in the hemispheric PSR size‐frequency distributions; such differences enable unique north/south migration diffusivities, which favor more water reaching the high northern latitudes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score0.205

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)0.0000.000

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 head0.037
GPT teacher head0.295
Teacher spread0.258 · 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

Citations30
Published2019
Admission routes2
Has abstractyes

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