MétaCan
Menu
Back to cohort
Record W2327707503 · doi:10.2514/6.2012-5240

Compact MRI for Astronaut Physiological Research and Medical Diagnosis

2012· article· en· W2327707503 on OpenAlexaff
Gordon E. Sarty, Saija Kontulainen, Adam Baxter‐Jones, Roger A. Pierson, K. Turek, André Obenaus, Bogusław Tomanek, Jonathan C. Sharp, Alan Scott, Louis Piche

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsCOM DEV InternationalNational Research Council Institute for BiodiagnosticsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceBiomedical engineeringEngineering

Abstract

fetched live from OpenAlex

The change and deterioration of an astronaut’s physiology during long-term space flight remains a significant concern. A magnetic resonance imager (MRI), that is available during space flight, would be a valuable tool that can be used to understand more fully the altered physiological processes, and to find ways of ameliorating those changes. The first application of an MRI in space would be to study the physiology of mass and strength change in bone and muscle. Such studies could be done on the International Space Station (ISS) with a new generation of Compact MRIs. Other applications include monitoring the formation of renal stones, monitoring the effects of radiation on the nervous and musculoskeletal systems, imaging body-wide fluid shifts, and imaging changes in cardiovascular morphology and function. Beyond basic research, an MRI in space, on the Moon or Mars would have the same clinical diagnostic utility as MRIs on Earth.

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 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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0190.004

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 head0.154
GPT teacher head0.461
Teacher spread0.307 · 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 designBench or experimental
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

Citations5
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicSpaceflight effects on biologyFrench-language works237,207