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Record W4399534639 · doi:10.1038/s41586-024-07586-8

A second space age spanning omics, platforms and medicine across orbits

2024· review· en· W4399534639 on OpenAlexaff
Christopher E. Mason, James Green, Konstantinos Adamopoulos, Evan E. Afshin, Jordan J. Baechle, Mathias Basner, Susan M. Bailey, Luca Bielski, Josef Borg, Joseph Borg, Jared T. Broddrick, Marissa Burke, Andrés Caicedo, Verónica Castañeda, Subhamoy Chatterjee, Christopher R. Chin, Sylvain V. Costes, Iwijn De Vlaminck, Rajeev I. Desai, Raja Dhir, Juan E. Diaz, Sofia Etlin, Zachary Feinstein, David Furman, J. Sebastian Garcia-Medina, Francine E. Garrett-Bakelman, Stefania Giacomello, Anjali Gupta, Amira Hassanin, Nadia Houerbi, Iris Irby, Emilia Javorsky, Peter Jirak, Christopher Jones, Khaled Y. Kamal, Brian D. Kangas, Fathi Karouia, JangKeun Kim, Joo hyun Kim, Ashley S. Kleinman, Try Lam, John M. Lawler, Jessica A. Lee, Charles L. Limoli, Alexander G. Lucaci, Matthew MacKay, J. Tyson McDonald, Ari Melnick, Cem Meydan, Jakub Mieczkowski, Masafumi Muratani, Deena Najjar, Mariam Othman, Eliah Overbey, Vera Paar, Jiwoon Park, Amber M. Paul, Adrian Perdyan, Jacqueline Proszynski, Robert J. Reynolds, April E. Ronca, Kate Rubins, Krista Ryon, Lauren Sanders, Patricia Glowe, Yash Shevde, Michael A. Schmidt, Ryan T. Scott, Bader Shirah, Karolina Sienkiewicz, Maria A. Sierra, Keith Siew, Corey A. Theriot, Braden Tierney, Kasthuri Venkateswaran, Jeremy Wain Hirschberg, Stephen B. Walsh, Claire Walter, Daniel A. Winer, Min Yu, Luis Zea, Jaime Mateus, Afshin Beheshti

Bibliographic record

VenueNature · 2024
Typereview
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersJapan Aerospace Exploration AgencyNational Institute on AgingNarodowa Agencja Wymiany AkademickiejGastro-Intestinal Research FoundationAgencia Nacional de Investigación y DesarrolloNational Aeronautics and Space AdministrationNational Institutes of HealthKidney Research UKNational Cancer InstituteScience and Technology Facilities CouncilJohnson Space CenterNational Institute of Mental HealthWorldQuant FoundationWellcome Trust
KeywordsAerospaceSpace explorationSpaceflightAstronauticsSpace medicineLeverage (statistics)Precision medicineHuman spaceflightInternational Space StationSpace (punctuation)Computer scienceRegulatory scienceData scienceSystems engineeringAeronauticsAerospace engineeringAviation medicineEngineeringMedicineArtificial intelligence

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.002

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 head0.033
GPT teacher head0.408
Teacher spread0.376 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractno

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