MétaCan
Menu
Back to cohort
Record W4234096042 · doi:10.1039/c9ta90244a

Correction: Hydrogels from natural egg white with extraordinary stretchability, direct-writing 3D printability and self-healing for fabrication of electronic sensors and actuators

2019· article· en· W4234096042 on OpenAlexaff
Qiang Chang, Mohammad Ali Darabi, Yuqing Liu, Yunfan He, Wen Zhong, Kibret Mequanint, Bingyun Li, Feng Lu, Malcolm Xing

Bibliographic record

VenueJournal of Materials Chemistry A · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAdvanced Computing and Algorithms
Canadian institutionsUniversity of WinnipegWestern UniversityUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
Fundersnot available
KeywordsFabricationSelf-healing hydrogelsMaterials scienceActuatorNatural (archaeology)NanotechnologyWhite (mutation)Composite materialChemistryPolymer chemistryEngineeringElectrical engineeringBiologyMedicine

Abstract

fetched live from OpenAlex

Correction for ‘Hydrogels from natural egg white with extraordinary stretchability, direct-writing 3D printability and self-healing for fabrication of electronic sensors and actuators’ by Qiang Chang et al. , J. Mater. Chem. A , 2019, DOI: 10.1039/c9ta06233e.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.040
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0490.026

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 head0.005
GPT teacher head0.256
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations5
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Materials Chemistry ASame topicAdvanced Computing and AlgorithmsFrench-language works237,207