MétaCan
Menu
Back to cohort

Nanocellulose-based Hydrogels for Biomedical Applications

2018· article· en· W2883371000 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

VenueCurrent Nanoscience · 2018
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSelf-healing hydrogelsNanocelluloseNanotechnologyNatural polymersMaterials scienceWound dressingPolymerEngineeringComposite materialChemical engineeringPolymer chemistryCellulose

Abstract

fetched live from OpenAlex

Hydrogels are three-dimensional polymer networks capable of absorbing and holding a large amount of water. They have a wide range of biomedical applications including drug carriers, biosensors, tissue scaffolds and wound dressings owning to their innate resemblance to the living tissue. Recently biodegradable and renewable natural polymers, especially nanocellulose, have gained immense attention in the development of hydrogels for biomedical applications. This review provides a brief analysis of the various nanocellulosic materials used in the fabrication of hydrogels for various biomedical applications. Recent developments in high performance hydrogels based on nanocellulose, including self-healing, highly tough and/or stretchable and 3D printable hydrogels will also be covered in this review.

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)0.001
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: none
Teacher disagreement score0.919
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.047
GPT teacher head0.361
Teacher spread0.315 · 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