MétaCan
Menu
Back to cohort
Record W2912751249 · doi:10.3389/frym.2019.00008

Biomaterials for Organ and Tissue Repair

2019· article· en· W2912751249 on OpenAlex
Caitlin Lazurko, Serena Harden, Erik J. Suuronen, Emilio I. Alarcón

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers for Young Minds · 2019
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsRegenerative medicineBiomaterialTissue engineeringHuman bodyBiomedical engineeringOrgan systemStem cellMedicineAnatomyBiologyPathologyCell biology

Abstract

fetched live from OpenAlex

Organs and tissues in the human body are quite resilient to the wear and tear of daily human life, but they can still fail for many reasons. Regenerative medicine is a field that explores new tools for repairing and replacing damaged organs and tissues. Regenerative medicine includes a vast array of treatments; for example, stem cell therapy, and biomaterials. Biomaterials are materials made to interact with the human body. They can be designed with many different materials for different applications and can be made structurally similar to the organ or tissue that needs to be repaired. The field of biomaterials is constantly evolving and as we continue to learn more about the interactions of biomaterials with the body on a cellular level, more and more biomaterial treatments are being developed.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.255
Teacher spread0.247 · 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