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Record W3200528799 · doi:10.32393/csme.2021.200

Sensitivity Study For Optimizing Electrospun Helix Fibers Production For Cardiac Scaffold

2021· article· en· W3200528799 on OpenAlex

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.

Bibliographic record

VenueProgress in Canadian Mechanical Engineering. Volume 4 · 2021
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsYork University
Fundersnot available
KeywordsSensitivity (control systems)ScaffoldProduction (economics)Helix (gastropod)Materials scienceBiomedical engineeringEngineeringElectronic engineeringBiology

Abstract

fetched live from OpenAlex

Cardiac tissue infraction is one of the leading factors to hospitalization or the cause of severe limitation in day-to-day activities. Due to the structure of the native cardiac tissue, depending on the native regeneration rate alone will not mitigate the damage incurred in the tissue. Naturally, research has shifted toward increasing the regeneration rate of the cardiac tissue. The regeneration rate can be amplified by utilizing Electrospun patches with seeded cardiac cells implanted on scared areas of the heart tissue. To increase the percentage of successful scaffold implantation the materials are carefully selected, and process parameters are varied to achieve the desired mechanical properties. Helical fibers are required to achieve the same stretch as the native cardiac tissue during expansion and contraction of the muscle. In this sensitivity study, the effect of various electrospinning parameters has been explored to optimize the production of helical fibers.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.247
Teacher spread0.239 · 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