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Record W4327662914 · doi:10.1109/ojemb.2023.3257991

EuniceScope: Low-Cost Imaging Platform for Studying Microgravity Cell Biology

2023· article· en· W4327662914 on OpenAlexaff
Wing Yan Chu, Kevin K. Tsia

Bibliographic record

VenueIEEE Open Journal of Engineering in Medicine and Biology · 2023
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReconfigurabilityNanotechnologyBiologyComputer scienceMaterials science

Abstract

fetched live from OpenAlex

Microgravity is proven to impact a wide range of human physiology, from stimulating stem cell differentiation to confounding cell health in bones, skeletal muscles, and blood cells. The research in this arena is progressively intensified by the increasing promises of human spaceflights. Considering the limited access to spaceflight, ground-based microgravity-simulating platforms have been indispensable for microgravity-biology research. However, they are generally complex, costly, hard to replicate and reconfigure - hampering the broad adoption of microgravity biology and astrobiology. To address these limitations, we developed a low-cost reconfigurable 3D-printed microscope coined EuniceScope to allow the democratization of astrobiology, especially for educational use. EuniceScope is a compact 2D clinostat system integrated with a modularized brightfield microscope, built upon 3D-printed toolbox. We demonstrated that this compact system offers plausible imaging quality and microgravity-simulating performance. Its high degree of reconfigurability thus holds great promise in the wide dissemination of microgravity-cell-biology research in the broader community, including Science, technology, engineering, and mathematics (STEM) educational and scientific community in the future.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.065
GPT teacher head0.371
Teacher spread0.306 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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