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Record W2068149454 · doi:10.1088/1758-5082/1/4/045005

Characterization of the flow behavior of alginate/hydroxyapatite mixtures for tissue scaffold fabrication

2009· article· en· W2068149454 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiofabrication · 2009
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaSaskatchewan Health Research Foundation
KeywordsFabricationScaffoldMaterials scienceBiomaterialBiomedical engineeringCharacterization (materials science)MicrostructureTissue engineeringFlow (mathematics)NanotechnologyComposite materialMechanics

Abstract

fetched live from OpenAlex

Mixtures of alginate and hydroxyapatite (HA) are promising materials for biomedical applications such as the fabrication of tissue scaffolds. In this paper, the flow behavior of alginate/HA mixtures was investigated and determined to be dependent on the concentration of both alginate and HA, and temperature. The relationships were mathematically established and verified with experimental results. As applied to the tissue scaffold fabrication, the flow rate of the biomaterial solution was predicted from the established flow behavior and verified by experiments. On this basis, the moving speed of the needle was determined and used in the tissue scaffold fabrication. The results obtained show that the knowledge of the flow behavior is essential to the fabrication of tissue scaffolds with an interconnected microstructure.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.277

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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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