MétaCan
Menu
Back to cohort
Record W4377565408 · doi:10.1111/jtxs.12765

Double‐nozzle <scp>3D</scp>‐printed bean paste buns: Effect of filling ratio and microwave heating time

2023· article· en· W4377565408 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

VenueJournal of Texture Studies · 2023
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsMcGill University
FundersNational Key Research and Development Program of ChinaHigher Education Discipline Innovation ProjectState Key Laboratory of Food Science and TechnologyGovernment of Jiangsu Province
KeywordsChewinessNozzleMicrowaveMaterials scienceFood scienceMicrowave powerExpansion ratioComposite materialChemistryEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

With the aggravation of the global aging process, more and more elderly people are facing the problem of dysphagia. The advantages of three-dimensional (3D) printing in making chewy food are increasingly prominent. In this study, the two-nozzle 3D printer was used to explore the effects of different proportions of buckwheat flour, printing filling ratio, microwave power, and time on the quality of bean-paste buns. The results showed that the bean paste filling containing 6% buckwheat flour had the best antioxidant and sensory properties. When the filling ratio was 21.6%, the microwave power was 560 W, and the time was 4 min, the obtained sample was the most satisfactory. Compared with the microwave-treated and steamed traditional samples, the chewiness of the samples was reduced by 52.43% and 15.14%, respectively, and the final product was easier to chew and swallow.

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: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.022
GPT teacher head0.286
Teacher spread0.264 · 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