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Record W6893321582 · doi:10.5281/zenodo.15136992

Gravity modeling of lunar lava tubes: Insights from Ape Cave as a terrestrial analogue

2025· dataset· en· W6893321582 on OpenAlexaff

Bibliographic record

VenueSummit (Simon Fraser University) · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsGeological Survey of CanadaSimon Fraser University
Fundersnot available
KeywordsCaveRegolithDensity contrastLavaImpact craterTransect

Abstract

fetched live from OpenAlex

The files presented here serve as a repository for the datasets generated during the research Gravity Modeling of Lunar Lava Tubes: Insights from Ape Cave as a Terrestrial Analogue. The material includes the following: Gravity_Field_Corrected.csv: contains the corrected gravity data collected from four transects at Ape Cave (WA, USA) in June 2023. Gravity_Forward_Ape_Cave.csv: synthetic results obtained from the gravity forward modeling of Ape Cave, using a density contrast of 2746 kg/m³ and a high-resolution 3D model of the cave. Gravity_Lunar_North.csv and Gravity_Lunar_South.csv: synthetic results obtained from the gravity forward modeling of a lunar lava tube with Ape Cave dimentions and a density contrast of 3270 kg/m³, a minimum depth of 26 m and two layers of regolith with a combined thickness of 5 m. The model was split into North and South sections due to model size constraints. Gravity_Lunar_Scaled_North.csv and Gravity_Lunar_Scaled_South.csv: synthetic results obtained from the gravity forward modeling of a lunar lava tube five times larger than Ape Cave, a density contrast of 3270 kg/m³, a minimum depth of 26 m and two layers of regolith with a combined thickness of 5 m. The model was split into North and South sections due to model size constraints.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0020.002
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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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
GenreDataset

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

Citations1
Published2025
Admission routes1
Has abstractyes

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