Lava Tube Analog Mission for Lunar Science and Human Performance Studies
Bibliographic record
Abstract
Samuel W. Ximenes and A. A. Mardon, G. Baiden, G. R. Osinski, N. Ghafoor, O. Gurtuna, A. Prevot, M. G. Daly. Exploration Architecture Corporation (XArc), 303 W. Sunset Rd. Ste. 108, San Antonio, TX 78209, USA, sximenes@explorationarchitecture.com, Antarctic Institute of Canada, Post Office Box 1223, Station Main, Edmonton, Alberta, Canada T5B 2W4, aamardon@yahoo.ca, Penguin Automated Systems, Inc., Naughton, Ontario, Canada, gbaiden@penguinasi.com, University of Western Ortario, Center for Planetary Science and Exploration, 1151 Richmond Street, London, Ontario, Canada N6A 5B7, gosinski@uwo.ca, MDA, 9445 Airport Rd., Bampton, Ontario, Canada L6S 4J3, nadeem.ghafoor@mdacorporation.comTurquoise Technology Solutions, Inc., 4999 St. Catherine West, Suite 540 Westmount, Canada, QC H3Z 1T3, gurtuna@turquoisetech.com, arthur.prevot@turquoisetech.com, York University, Department of Earth and Space Science & Engineering , Toronto, Ontario, Canada, M3J 1P3, dalym@yorku.ca.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".